VANTAGE-Bench update: prompts + question-only annotations + Vantage2DPointing rename + test split + data/README + CHANGELOG

CHANGELOG.md

@@ -0,0 +1,28 @@
+# Changelog
+
+All notable changes to **`nvidia/PhysicalAI-VANTAGE-Bench`** on Hugging Face.
+
+## 2026-05-15
+
+- Per-task `prompt.json` added under `data/2dbbox/` and
+  `data/dense_captioning/` (schema: `{"prompt": "<text>"}`).
+- Question-only annotation JSONs added under
+  `data/vqa/data_jsons/annotations/`,
+  `data/temporal_localization/data_jsons/annotations/`,
+  `data/event_verification/filtered/**`,
+  and `data/referring/refdrone_test_llava.json`. Answer fields
+  (`gt`, `gt_option`, `answer`, GT bbox coordinates, etc.) are
+  stripped — only the question side ships.
+- `Metropolis2DPointing` references renamed to `Vantage2DPointing`.
+- New `data/README.md` summarizing the dataset layout and listing the
+  per-task prompts.
+
+## 2026-05-09
+- **`data/` restructured.** Top-level task directories renamed:
+  `dense_captioning/`, `event_verification/`, `pointing/`,
+  `referring/`, `temporal_localization/`, `vqa/`. The legacy
+  `Spatial/` directory was dropped.
+
+## 2026-05-04
+- **Initial release**: full release uploaded to
+  `nvidia/PhysicalAI-VANTAGE-Bench` under `data/` (PR #2).

README.md

@@ -2,6 +2,10 @@
 license: other
 license_name: nvidia-evaluation-data-license
 license_link: LICENSE.md
+dataset_info:
+  splits:
+    - name: test
+      num_examples: 35027
 ---
 
 # VANTAGE-BENCH

data/2dbbox/prompt.json

@@ -0,0 +1,3 @@
+{
+  "prompt": "Locate every instance that belongs to the following categories: 'person'. For each instance of the class, report bbox coordinates in JSON format. Do not group instances and report only individual instances. Avoid reporting duplicate instances."
+}

data/README.md

@@ -0,0 +1,68 @@
+# VANTAGE-Bench — `data/`
+
+Brief overview of the dataset structure and per-task prompts.
+Ground-truth answers are held server-side; only the **question side** of
+each annotation ships here.
+
+## Layout
+
+```
+data/
+├── 2dbbox/                       # 2D bounding-box detection
+│   ├── prompt.json
+│   └── <sequence>/images/*.jpg
+├── dense_captioning/             # Dense video captioning
+│   ├── prompt.json
+│   └── *.mp4
+├── event_verification/           # Binary event classification
+│   └── filtered/
+│       ├── metropolis_event_verification/{*.mp4, test_annotation.json}
+│       ├── tailgating/{location_a, location_b}/{*.mp4, test_annotation.json}
+│       └── warehouse_near_miss/{*.mp4, test_annotations.json}
+├── pointing/                     # 2D spatial pointing
+│   └── Vantage2DPointing.tsv
+├── referring/                    # 2D referring expressions
+│   └── refdrone_test_llava.json
+├── temporal_localization/        # Temporal grounding
+│   ├── *.mp4
+│   └── data_jsons/annotations/*.json
+├── tracking/                     # Stateless single-object tracking
+│   └── sot_benchmark.jsonl
+└── vqa/                          # Video question answering
+    ├── *.mp4
+    └── data_jsons/annotations/*.json
+```
+
+## Per-task prompts
+
+Tasks without a per-entry `question` field carry a top-level
+`prompt.json` with the model instruction (schema: `{"prompt": "<text>"}`).
+
+### `2dbbox/` — 2D Detection
+> Locate every instance that belongs to the following categories: `person`. For each instance of the class, report bbox coordinates in JSON format. Do not group instances and report only individual instances. Avoid reporting duplicate instances.
+
+### `dense_captioning/` — Dense Video Captioning
+> Describe the notable events in the provided video. Provide the result in json format with `mm:ss.ff` format for time depiction for each event. Use keywords `start`, `end` and `caption` in the json output.
+
+### `vqa/` — Video Question Answering
+Per-entry questions in `vqa/data_jsons/annotations/*.json`. Each entry has `{q_uid, question, options, …}`; answer keys (`gt`, `gt_option`, `*_update_*`, etc.) are removed.
+
+### `temporal_localization/` — Temporal Grounding
+Per-entry questions in `temporal_localization/data_jsons/annotations/*.json`. Each entry has `{vid, question_id, question, duration, …}`; the `answer` timestamps are removed.
+
+### `event_verification/` — Binary Event Verification
+- **bcq format** (`metropolis_event_verification/`, `tailgating/location_a/`, `tailgating/location_b/`): top-level key `"bcq"` is a list of `{id, video, system_prompt, question}`; the binary `answer` is removed.
+- **simple format** (`warehouse_near_miss/`): a list of `{id, video_id, question}`; the binary `answer` is removed.
+
+### `pointing/` — 2D Spatial Pointing
+`Vantage2DPointing.tsv` — tab-separated. Each row carries the question and multiple-choice options; the last two ground-truth columns are dropped.
+
+### `referring/` — 2D Referring Expressions
+`refdrone_test_llava.json` — list of LLaVA conversation entries. Only the `human` turn (the question) is retained; the `gpt` turn (predicted bboxes) and GT meta fields are removed.
+
+### `tracking/` — Stateless Single-Object Tracking
+`sot_benchmark.jsonl` — one JSON object per clip with `seq_id`, `scene`, `camera`, `init_bbox` (the seed bounding box you're given as input), `init_frame_id`, and `canonical_frame_ids` (the frames you must predict at). Ground-truth trajectories are held server-side.
+
+## Submitting predictions
+
+See the top-level `README.md` for the eval-server instructions per task.

data/dense_captioning/prompt.json

@@ -0,0 +1,3 @@
+{
+  "prompt": "Describe the notable events in the provided video. Provide the result in json format with 'mm:ss.ff' format for time depiction for each event. Use keywords 'start', 'end' and 'caption' in the json output."
+}

data/event_verification/filtered/metropolis_event_verification/test_annotation.json

@@ -0,0 +1,406 @@
+{
+  "bcq": [
+    {
+      "id": "traffic_chunks/LUPZNgg5idk_13",
+      "video": "traffic_chunks/LUPZNgg5idk_13.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/IpgfZf6Y2BE_14",
+      "video": "traffic_chunks/IpgfZf6Y2BE_14.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/IpgfZf6Y2BE_15",
+      "video": "traffic_chunks/IpgfZf6Y2BE_15.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/NOALQmAB4yE_16",
+      "video": "traffic_chunks/NOALQmAB4yE_16.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a vehicle collide with pedestrian?"
+    },
+    {
+      "id": "traffic_chunks/SEb7p5oszeM_17",
+      "video": "traffic_chunks/SEb7p5oszeM_17.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a vehicle collide with a cyclist?"
+    },
+    {
+      "id": "traffic_chunks/SEb7p5oszeM_18",
+      "video": "traffic_chunks/SEb7p5oszeM_18.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a vehicle collide with a pedestrian?"
+    },
+    {
+      "id": "traffic_chunks/MmsgbcpWn-k_19",
+      "video": "traffic_chunks/MmsgbcpWn-k_19.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/MmsgbcpWn-k_20",
+      "video": "traffic_chunks/MmsgbcpWn-k_20.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/MmsgbcpWn-k_21",
+      "video": "traffic_chunks/MmsgbcpWn-k_21.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "traffic_chunks/NOALQmAB4yE_24",
+      "video": "traffic_chunks/NOALQmAB4yE_24.mp4",
+      "system_prompt": "You are a traffic monitoring system analyzing video of a street. Determine if a collision between vehicles, or vehicle and pedestrian or vehicle and cyclist has likely occurred.\nThe clip may not show the exact moment of impact, so use post-event evidence such as:\n- Vehicles in contact or showing visible damage (dents, debris, smoke, broken parts).\n- Pedestrian or cyclist on the ground, struck, or showing clear signs of impact or distress.. \n- Abnormal positions (intersecting, facing opposite directions, one against the side/rear of another).\n- Stationary vehicles remaining in contact or stopped in unnatural alignment.\n- Behavior inconsistent with normal driving (sudden halt, failure to separate, blocked motion).\n- Other unusual signs (e.g., airbags, leaking fluids, shattered glass) can also support the conclusion.\nA collision is \u201clikely\u201d if two or more independent cues strongly indicate impact, even if the collision itself is not shown. If evidence is weak or ambiguous, do not assume a collision.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a collision occur between two or more vehicles?"
+    },
+    {
+      "id": "safety_chunks/evs_134db13b21",
+      "video": "safety_chunks/evs_134db13b21.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_99c1cd175d",
+      "video": "safety_chunks/evs_99c1cd175d.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_8cc3cd0258",
+      "video": "safety_chunks/evs_8cc3cd0258.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_bc929d97da",
+      "video": "safety_chunks/evs_bc929d97da.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_d897e4ada3",
+      "video": "safety_chunks/evs_d897e4ada3.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_c3e684b820",
+      "video": "safety_chunks/evs_c3e684b820.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_17560f2666",
+      "video": "safety_chunks/evs_17560f2666.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_0f0c53aa1c",
+      "video": "safety_chunks/evs_0f0c53aa1c.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_405dd1e5f8",
+      "video": "safety_chunks/evs_405dd1e5f8.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_8f5ae5b865",
+      "video": "safety_chunks/evs_8f5ae5b865.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_50815b9c8c",
+      "video": "safety_chunks/evs_50815b9c8c.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/tailgating_13",
+      "video": "safety_chunks/tailgating_13.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_866549be90",
+      "video": "safety_chunks/evs_866549be90.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_e5ccfbd6bd",
+      "video": "safety_chunks/evs_e5ccfbd6bd.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_d2523c5c64",
+      "video": "safety_chunks/evs_d2523c5c64.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_f717d6dd57",
+      "video": "safety_chunks/evs_f717d6dd57.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_110cbe0aac",
+      "video": "safety_chunks/evs_110cbe0aac.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is anyone fighting?"
+    },
+    {
+      "id": "safety_chunks/evs_8e472b1db0",
+      "video": "safety_chunks/evs_8e472b1db0.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is anyone fighting?"
+    },
+    {
+      "id": "safety_chunks/evs_abf9d9bc50",
+      "video": "safety_chunks/evs_abf9d9bc50.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_c950abf04f",
+      "video": "safety_chunks/evs_c950abf04f.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/Security_3_22",
+      "video": "safety_chunks/Security_3_22.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is anyone fighting?"
+    },
+    {
+      "id": "safety_chunks/Security_2_23",
+      "video": "safety_chunks/Security_2_23.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/Security_2_24",
+      "video": "safety_chunks/Security_2_24.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/Security_2_25",
+      "video": "safety_chunks/Security_2_25.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Entering is from left to right and exiting is from right to left, is anyone exiting with a cart full of equipment?"
+    },
+    {
+      "id": "safety_chunks/Security_2_26",
+      "video": "safety_chunks/Security_2_26.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Entering is from left to right and exiting is from right to left, is anyone entering with a cart full of equipment?"
+    },
+    {
+      "id": "safety_chunks/GX010011_Clip_8_27",
+      "video": "safety_chunks/GX010011_Clip_8_27.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the hallway overcrowded?"
+    },
+    {
+      "id": "safety_chunks/GX010011_Clip_9_28",
+      "video": "safety_chunks/GX010011_Clip_9_28.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does everyone scan a badge to enter the room?"
+    },
+    {
+      "id": "safety_chunks/evs_f262e9ed6a",
+      "video": "safety_chunks/evs_f262e9ed6a.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_48a0587066",
+      "video": "safety_chunks/evs_48a0587066.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "safety_chunks/evs_191151ccf4",
+      "video": "safety_chunks/evs_191151ccf4.mp4",
+      "system_prompt": "You are a security monitoring system analyzing video of a access gate and hallways. \n\nGate Monitoring:\nWhen monitoring access gate, people are required to badge, the gate unlocks, and they enter. Determine whether a tailgating/piggybacking event has likely occurred (i.e., one or more people enter on a single authorization without individually badging).\nThe video clip shows the badge reader. Infer from post-event evidence such as:\n- Single gate-open event while multiple people pass through before the gate closes.\n- A follower enters closely behind the badged person (minimal gap in time or distance) without stopping at the reader or making a clear badging gesture.\n- The gate is held open/propped, or gate-open duration is longer than typical for a single entrant.\n- Multiple people cross the threshold during one gate cycle (gate does not close between them).\n- The leader looks back/holds the door while the follower does not badge.\n\nHallway Monitoring: \n- At the hallways look for fights and overcrowding.\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did a person tailgate through the access gate without badging?"
+    },
+    {
+      "id": "warehouse_chunks/Warehouse_240219_GoPro_7_GX070600_100_3_2",
+      "video": "warehouse_chunks/Warehouse_240219_GoPro_7_GX070600_100_3_2.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are all workers wearing PPE (hardhats and safety vests)?"
+    },
+    {
+      "id": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_500_2_3",
+      "video": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_500_2_3.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the path obstructed for the forklift to pass?"
+    },
+    {
+      "id": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_500_1_4",
+      "video": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_500_1_4.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are the boxes properly stacked on the pallet loaded on the forklift?"
+    },
+    {
+      "id": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_400_1_5",
+      "video": "warehouse_chunks/Warehouse_240219_GoPro_7_GX010600_400_1_5.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the path obstructed for the forklift to pass?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_600_4_6",
+      "video": "warehouse_chunks/warehouse_1_600_4_6.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Did anyone experience a fall or end up on the ground?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_540_7",
+      "video": "warehouse_chunks/warehouse_1_540_7.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is any person near or in close proximity to the box when it falls?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_540_4_8",
+      "video": "warehouse_chunks/warehouse_1_540_4_8.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the operator or a person using a cell phone while working?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_425_6_9",
+      "video": "warehouse_chunks/warehouse_1_425_6_9.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the operator or a person jumping from the ladder?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_0_0_10",
+      "video": "warehouse_chunks/concat_wh_52_0_0_10.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does any box fall off the robot?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_425_4_11",
+      "video": "warehouse_chunks/warehouse_1_425_4_11.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are the boxes properly stacked as the operator lifts?"
+    },
+    {
+      "id": "warehouse_chunks/warehouse_1_120_12",
+      "video": "warehouse_chunks/warehouse_1_120_12.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does the operator throw any boxes?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_0_5_13",
+      "video": "warehouse_chunks/concat_wh_52_0_5_13.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does a box fall off the robot?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_0_14",
+      "video": "warehouse_chunks/concat_wh_52_300_0_14.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does anyone walk in front of the forklift?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_1_15",
+      "video": "warehouse_chunks/concat_wh_52_300_1_15.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Does anyone walk in front of the forklift?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_2_16",
+      "video": "warehouse_chunks/concat_wh_52_300_2_16.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is the path of the forklift clear?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_2_17",
+      "video": "warehouse_chunks/concat_wh_52_300_2_17.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are the boxes properly stacked on the pallet that is loaded on the forklift?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_1_18",
+      "video": "warehouse_chunks/concat_wh_52_300_1_18.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are all workers wearing PPE?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_300_3_19",
+      "video": "warehouse_chunks/concat_wh_52_300_3_19.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are the boxes properly stacked on the pallet that is loaded on the forklift?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_0_20",
+      "video": "warehouse_chunks/concat_wh_52_1890_0_20.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are all workers wearing PPE?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_4_21",
+      "video": "warehouse_chunks/concat_wh_52_1890_4_21.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are any boxes crushed?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_4_22",
+      "video": "warehouse_chunks/concat_wh_52_1890_4_22.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Do any boxes get dropped?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_5_23",
+      "video": "warehouse_chunks/concat_wh_52_1890_5_23.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Are any boxes crushed?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_5_24",
+      "video": "warehouse_chunks/concat_wh_52_1890_5_24.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Do any boxes get dropped?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_1890_9_25",
+      "video": "warehouse_chunks/concat_wh_52_1890_9_25.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is everyone wearing a hardhat and safety vest?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_2925_1_26",
+      "video": "warehouse_chunks/concat_wh_52_2925_1_26.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Do any boxes get dropped?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_2925_27",
+      "video": "warehouse_chunks/concat_wh_52_2925_27.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is anything blocking the path of the small yellow robot?"
+    },
+    {
+      "id": "warehouse_chunks/concat_wh_52_2925_28",
+      "video": "warehouse_chunks/concat_wh_52_2925_28.mp4",
+      "system_prompt": "You are a warehouse monitoring system analyzing video footage. Your task is to answer safety and compliance questions strictly with \"Yes\" or \"No\".\nThe clip may not show the entire event, so rely on visible evidence. Infer from post-event evidence: \n- PPE compliance (hardhats, safety vests, etc.).\n- Path clear or obstructed for forklifts or robots.\n- Boxes stacked properly on pallets or being lifted.\n- Boxes crushed, dropped, or falling off forklifts/robots/operators.\n- Operator behavior (falling, using cell phone, throwing boxes).\n- Human safety risks (walking in front of forklift, near falling boxes, jumping from ladders).\n\nConfirm \"Yes\" only when visual evidence is clear.\nOtherwise, answer \"No\".",
+      "question": "Is anything blocking the path of the forklift?"
+    }
+  ]
+}

data/event_verification/filtered/tailgating/location_a/test_annotation.json

@@ -0,0 +1,172 @@
+{
+  "bcq": [
+    {
+      "id": "videos/site_1/category_tailgate/10_15_2025_sp_8_35_08_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_15_2025_sp_8_35_08_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_20_2025_sp_4_13_08_sp_PM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_20_2025_sp_4_13_08_sp_PM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_24_2025_sp_9_44_18_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_24_2025_sp_9_44_18_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_20_2025_sp_1_01_55_sp_PM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_20_2025_sp_1_01_55_sp_PM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_30_25_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_30_25_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_15_2025_sp_8_38_19_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_15_2025_sp_8_38_19_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_8_2025_sp_8_38_03_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_8_2025_sp_8_38_03_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_09_05_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_09_05_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_46_58_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_46_58_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_24_2025_sp_10_23_17_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_24_2025_sp_10_23_17_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_9_2025_sp_8_48_30_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_9_2025_sp_8_48_30_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_8_2025_sp_8_43_54_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_8_2025_sp_8_43_54_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/11_21_2025_sp_11_56_03_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_tailgate/11_21_2025_sp_11_56_03_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_16_2025_sp_9_00_13_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_16_2025_sp_9_00_13_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_48_55_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_48_55_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_12_31_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_12_31_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_16_2025_sp_9_10_29_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_16_2025_sp_9_10_29_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_15_2025_sp_8_39_16_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_15_2025_sp_8_39_16_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_24_2025_sp_10_44_28_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_24_2025_sp_10_44_28_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_24_2025_sp_1_28_55_sp_PM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_24_2025_sp_1_28_55_sp_PM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_32_48_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_32_48_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_21_2025_sp_11_40_45_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_21_2025_sp_11_40_45_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_15_08_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_15_08_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_25_2025_sp_6_22_57_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_25_2025_sp_6_22_57_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/11_21_2025_sp_11_55_17_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_tailgate/11_21_2025_sp_11_55_17_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_tailgate/10_9_2025_sp_8_51_57_sp_PM_sp__lp_UTC-07_00_rp_",
+      "video": "videos/site_1/category_tailgate/10_9_2025_sp_8_51_57_sp_PM_sp__lp_UTC-07_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_21_2025_sp_11_18_34_sp_AM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_21_2025_sp_11_18_34_sp_AM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/site_1/category_badge/11_24_2025_sp_2_57_33_sp_PM_sp__lp_UTC-08_00_rp_",
+      "video": "videos/site_1/category_badge/11_24_2025_sp_2_57_33_sp_PM_sp__lp_UTC-08_00_rp_.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    }
+  ]
+}

data/event_verification/filtered/tailgating/location_b/test_annotation.json

@@ -0,0 +1,136 @@
+{
+  "bcq": [
+    {
+      "id": "videos/redacted/evs_6a52f11dad",
+      "video": "videos/redacted/evs_6a52f11dad.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_b561420691",
+      "video": "videos/redacted/evs_b561420691.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_907fe737cf",
+      "video": "videos/redacted/evs_907fe737cf.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_0ea91247d8",
+      "video": "videos/redacted/evs_0ea91247d8.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_6ad1a891ad",
+      "video": "videos/redacted/evs_6ad1a891ad.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_d0e459f682",
+      "video": "videos/redacted/evs_d0e459f682.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_2e30648c0a",
+      "video": "videos/redacted/evs_2e30648c0a.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_292daa255e",
+      "video": "videos/redacted/evs_292daa255e.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_a9e180fff3",
+      "video": "videos/redacted/evs_a9e180fff3.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_53f64ccbe8",
+      "video": "videos/redacted/evs_53f64ccbe8.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_b982d3f339",
+      "video": "videos/redacted/evs_b982d3f339.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_03018e0ecf",
+      "video": "videos/redacted/evs_03018e0ecf.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_bf746e9608",
+      "video": "videos/redacted/evs_bf746e9608.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_6e738337bc",
+      "video": "videos/redacted/evs_6e738337bc.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_f979eb0318",
+      "video": "videos/redacted/evs_f979eb0318.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_024ae78480",
+      "video": "videos/redacted/evs_024ae78480.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_fa68a5a4f8",
+      "video": "videos/redacted/evs_fa68a5a4f8.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_eed8192951",
+      "video": "videos/redacted/evs_eed8192951.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_32231b0bd6",
+      "video": "videos/redacted/evs_32231b0bd6.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_a713802c9d",
+      "video": "videos/redacted/evs_a713802c9d.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_3f674e8c19",
+      "video": "videos/redacted/evs_3f674e8c19.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    },
+    {
+      "id": "videos/redacted/evs_6a4da56832",
+      "video": "videos/redacted/evs_6a4da56832.mp4",
+      "system_prompt": "You are an expert AI assistant for video analysis. Your task is to accurately classify whether a surveillance video depicts **normal access** or **tailgating** behavior, based on the strict definitions below.\n\n# Category Definitions\n\n- **Normal Access**:  \nA person (or group) enters a secure area **properly authenticated** (e.g., by using a badge, keycard, or biometric system). Only those with authorization enter. There is **no evidence of security breach** or abnormal entry.  \n\n- **Tailgating**:  \nOne or more individuals **enter a secure area without authorization** by following closely behind an authorized person who has legitimately gained access. The unauthorized individual does not use valid credentials but takes advantage of the door being open. This includes:  \n- Following immediately behind someone with a badge swipe.  \n- Entering as part of a group where only the first person authenticates.  \n- Entering without any visible authentication while leveraging someone else's access.",
+      "question": "Does the video depict tailgating behavior? Answer \"Yes\" or \"No\"."
+    }
+  ]
+}

data/event_verification/filtered/warehouse_near_miss/test_annotations.json

@@ -0,0 +1,232 @@
+[
+  {
+    "id": "positive/scene_07_01_00-23-52_to_00-25-33_GoPro1_Fork_Lift_stopped_while_person_crossing_the_isle_08-22",
+    "video_id": "positive/scene_07_01_00-23-52_to_00-25-33_GoPro1_Fork_Lift_stopped_while_person_crossing_the_isle_08-22.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_08_01_00-00-46_to_00-02-20_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_01-15",
+    "video_id": "positive/scene_08_01_00-00-46_to_00-02-20_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_01-15.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_08_02_00-02-20_to_00-04-56_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_08-22",
+    "video_id": "positive/scene_08_02_00-02-20_to_00-04-56_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_08-22.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_08_03_00-04-56_to_00-08-15_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_04-18",
+    "video_id": "positive/scene_08_03_00-04-56_to_00-08-15_GoPro1_Fork_Lift_crossing_while_person_running_in_front_of_it_04-18.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_09_01_00-08-15_to_00-10-22_GoPro1_Fork_Lift_moving_while_person_on_the_phone_crossing_the_isle_06-20",
+    "video_id": "positive/scene_09_01_00-08-15_to_00-10-22_GoPro1_Fork_Lift_moving_while_person_on_the_phone_crossing_the_isle_06-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_10_01_00-10-22_to_00-13-12_GoPro1_Fork_Lift_moving_while_person_crossing_the_isle_06-20",
+    "video_id": "positive/scene_10_01_00-10-22_to_00-13-12_GoPro1_Fork_Lift_moving_while_person_crossing_the_isle_06-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_11_01_00-13-12_to_00-16-16_GoPro1_Fork_Lift_moving_while_multiple_people_in_the_scene_04-22",
+    "video_id": "positive/scene_11_01_00-13-12_to_00-16-16_GoPro1_Fork_Lift_moving_while_multiple_people_in_the_scene_04-22.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_12_01_00-16-16_to_00-17-31_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_06-20",
+    "video_id": "positive/scene_12_01_00-16-16_to_00-17-31_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_06-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_12_02_00-17-31_to_00-19-50_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_02-16",
+    "video_id": "positive/scene_12_02_00-17-31_to_00-19-50_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_02-16.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_14_01_00-19-50_to_00-22-54_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_04-18",
+    "video_id": "positive/scene_14_01_00-19-50_to_00-22-54_GoPro1_Fork_Lift_moving_while_person_walking_behind_the_forklift_04-18.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_16_01_00-22-54_to_00-25-38_GoPro1_person_walking_in_front_of_fork_lift_04-48",
+    "video_id": "positive/scene_16_01_00-22-54_to_00-25-38_GoPro1_person_walking_in_front_of_fork_lift_04-48.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_17_01_00-25-38_to_00-27-32_GoPro1_person_running_in_front_of_fork_lift_02-16",
+    "video_id": "positive/scene_17_01_00-25-38_to_00-27-32_GoPro1_person_running_in_front_of_fork_lift_02-16.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_17_02_00-27-32_to_00-30-55_GoPro1_person_running_in_front_of_fork_lift_02-26",
+    "video_id": "positive/scene_17_02_00-27-32_to_00-30-55_GoPro1_person_running_in_front_of_fork_lift_02-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_18_01_00-30-55_to_00-33-57_GoPro1_person_jumping_to_not_get_hit_by_the_forklift_00-20",
+    "video_id": "positive/scene_18_01_00-30-55_to_00-33-57_GoPro1_person_jumping_to_not_get_hit_by_the_forklift_00-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_19_01_00-33-57_to_00-35-41_GoPro1_fork_lift_moving_backwards_person_cutting_in_front_of_the_fo_04-24",
+    "video_id": "positive/scene_19_01_00-33-57_to_00-35-41_GoPro1_fork_lift_moving_backwards_person_cutting_in_front_of_the_fo_04-24.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_20_01_00-35-41_to_00-37-23_GoPro1_fork_lift_going_backwards_person_running_passed_04-22",
+    "video_id": "positive/scene_20_01_00-35-41_to_00-37-23_GoPro1_fork_lift_going_backwards_person_running_passed_04-22.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_21_01_00-37-23_to_00-39-30_GoPro1_fork_lift_going_backwards_person_stops_06-24",
+    "video_id": "positive/scene_21_01_00-37-23_to_00-39-30_GoPro1_fork_lift_going_backwards_person_stops_06-24.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_22_01_00-39-30_to_00-45-36_GoPro1_fork_lift_moving_person_hesitating_and_stepping_back_01-20",
+    "video_id": "positive/scene_22_01_00-39-30_to_00-45-36_GoPro1_fork_lift_moving_person_hesitating_and_stepping_back_01-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_23_01_00-45-36_to_00-49-03_GoPro1_boxes_blocking_the_view_of_the_driver_and_person_crossing_06-26",
+    "video_id": "positive/scene_23_01_00-45-36_to_00-49-03_GoPro1_boxes_blocking_the_view_of_the_driver_and_person_crossing_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_25_01_00-02-02_to_00-05-05_GoPro1_person_working_on_boxes_while_fork_lift_approaches_06-30",
+    "video_id": "positive/scene_25_01_00-02-02_to_00-05-05_GoPro1_person_working_on_boxes_while_fork_lift_approaches_06-30.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_26_01_00-05-05_to_00-08-50_GoPro1_same_as_above_person_jumping_05-24",
+    "video_id": "positive/scene_26_01_00-05-05_to_00-08-50_GoPro1_same_as_above_person_jumping_05-24.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "positive/scene_27_01_00-08-50_to_00-15-25_GoPro1_person_bending_down_fork_lift_moving_forward_10-30",
+    "video_id": "positive/scene_27_01_00-08-50_to_00-15-25_GoPro1_person_bending_down_fork_lift_moving_forward_10-30.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T1_C2_CS_S13T1_1-12-11-59_chunk_5__event_005_5",
+    "video_id": "negative/Scene_13_S13T1_C2_CS_S13T1_1-12-11-59_chunk_5__event_005_5.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T3_C5_AS_S13T3_01-18-12-10_chunk_2__event_001_1",
+    "video_id": "negative/Scene_13_S13T3_C5_AS_S13T3_01-18-12-10_chunk_2__event_001_1.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C4_AS_S13T4_1-12-12-28_chunk_5__event_008_8",
+    "video_id": "negative/Scene_13_S13T4_C4_AS_S13T4_1-12-12-28_chunk_5__event_008_8.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C4_AS_S13T4_1-12-12-28_chunk_6__event_001_1",
+    "video_id": "negative/Scene_13_S13T4_C4_AS_S13T4_1-12-12-28_chunk_6__event_001_1.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C5_AS_S13T4_00-58-12-14_chunk_5__event_004_4",
+    "video_id": "negative/Scene_13_S13T4_C5_AS_S13T4_00-58-12-14_chunk_5__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_1__event_004_4",
+    "video_id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_1__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_5__event_004_4",
+    "video_id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_5__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_6__event_001_1",
+    "video_id": "negative/Scene_13_S13T4_C6_AS_S13T4_00-43-11-59_chunk_6__event_001_1.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T5_C2_AS_S13T5_0-51-11-39_chunk_5__event_004_4",
+    "video_id": "negative/Scene_13_S13T5_C2_AS_S13T5_0-51-11-39_chunk_5__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T5_C3_AS_S13T5_0-53-11-38_chunk_5__event_004_4",
+    "video_id": "negative/Scene_13_S13T5_C3_AS_S13T5_0-53-11-38_chunk_5__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T5_C4_AS_S13T5_0-53-11-39_chunk_5__event_004_4",
+    "video_id": "negative/Scene_13_S13T5_C4_AS_S13T5_0-53-11-39_chunk_5__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_13_S13T5_C6_AS_S13T5_00-29-11-15_chunk_5__event_003_3",
+    "video_id": "negative/Scene_13_S13T5_C6_AS_S13T5_00-29-11-15_chunk_5__event_003_3.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_4_S4T4_C2_CV_S4T4_00-54-10-48_chunk_5__event_001_1",
+    "video_id": "negative/Scene_4_S4T4_C2_CV_S4T4_00-54-10-48_chunk_5__event_001_1.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_4_S4T4_C4_CS_S4T4_00-54-10-48_chunk_4__event_003_3",
+    "video_id": "negative/Scene_4_S4T4_C4_CS_S4T4_00-54-10-48_chunk_4__event_003_3.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_4_S4T4_C6_CS_S4T4_00-54-10-48_chunk_1__event_004_4",
+    "video_id": "negative/Scene_4_S4T4_C6_CS_S4T4_00-54-10-48_chunk_1__event_004_4.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/Scene_4_S4T4_C6_CS_S4T4_00-54-10-48_chunk_5__event_002_2",
+    "video_id": "negative/Scene_4_S4T4_C6_CS_S4T4_00-54-10-48_chunk_5__event_002_2.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_01_01_00-00-00_to_00-07-34_GoPro1_Calibration_with_people_walking_around_06-26",
+    "video_id": "negative/scene_01_01_00-00-00_to_00-07-34_GoPro1_Calibration_with_people_walking_around_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_02_01_00-07-34_to_00-11-38_GoPro1_Forklifts_being_moved_out_of_the_way_06-26",
+    "video_id": "negative/scene_02_01_00-07-34_to_00-11-38_GoPro1_Forklifts_being_moved_out_of_the_way_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_04_01_00-11-38_to_00-19-46_GoPro1_Forklift_entering_the_aisle_no_pedestrians_around_06-26",
+    "video_id": "negative/scene_04_01_00-11-38_to_00-19-46_GoPro1_Forklift_entering_the_aisle_no_pedestrians_around_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_05_01_00-19-46_to_00-21-03_GoPro1_Fork_Lift_crossing_people_crossing_afterwards_06-26",
+    "video_id": "negative/scene_05_01_00-19-46_to_00-21-03_GoPro1_Fork_Lift_crossing_people_crossing_afterwards_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_06_01_00-21-03_to_00-23-52_GoPro1_Fork_Lift_crossing_people_following_the_forklift_06-26",
+    "video_id": "negative/scene_06_01_00-21-03_to_00-23-52_GoPro1_Fork_Lift_crossing_people_following_the_forklift_06-26.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_28_01_00-15-25_to_00-27-58_GoPro1_boxes_falling_04-22",
+    "video_id": "negative/scene_28_01_00-15-25_to_00-27-58_GoPro1_boxes_falling_04-22.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_29_01_00-01-07_to_00-10-18_GoPro1_driver_picks_up_trash_00-50",
+    "video_id": "negative/scene_29_01_00-01-07_to_00-10-18_GoPro1_driver_picks_up_trash_00-50.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  },
+  {
+    "id": "negative/scene_29_01_00-09-27_to_00-12-53_GoPro1_driver_picks_up_trash_00-20",
+    "video_id": "negative/scene_29_01_00-09-27_to_00-12-53_GoPro1_driver_picks_up_trash_00-20.mp4",
+    "question": "Please tell whether the video contains near-miss collision between person and forklift. Your final answer should be either Yes or No."
+  }
+]

data/temporal_localization/data_jsons/annotations/nv_lita_benchmark.json

@@ -0,0 +1,272 @@
+[
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_400",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_400.mp4_0",
+    "question": "At what time in the video does \"A man drives a forklift behind three workers\" take place? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_400",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_400.mp4_1",
+    "question": "At what time in the video does \"Three factory workers in green vests wearing yellow hats talk\" take place? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_400",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_400.mp4_2",
+    "question": "When does \"The three workers move out of the way of the forklift\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_400",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_400.mp4_3",
+    "question": "When does \"The forklift moves forward and rotates 90 degrees in front of a shelf of boxes\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_400",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_400.mp4_4",
+    "question": "At what time in the video does \"The forklift lifts its arm and inserts it into a shelf of boxes\" take place? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_500",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_500.mp4_0",
+    "question": "When is \"A forklift's bars go underneath boxes on a shelf\" depicted in the video? Provide a response using only start and end timestamps.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_500",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_500.mp4_1",
+    "question": "When is \"The forklift slowly takes the boxes off of the shelf\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "Warehouse_240219_GoPro_7_GX010600_500",
+    "question_id": "Warehouse_240219_GoPro_7_GX010600_500.mp4_2",
+    "question": "When does \"The forklift drives into the distance\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.026633
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_0",
+    "question": "When is \"A box falls\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_1",
+    "question": "At what point in the video does \"The forklift waits for the robot in its way\" happen? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_2",
+    "question": "At what point in the video does \"A worker walks to the box\" happen? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_3",
+    "question": "When does \"A lady picks up the box\" happen in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_4",
+    "question": "At what point in the video does \"The worker walks back\" happen? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_5",
+    "question": "At what point in the video does \"The box falls again\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_6",
+    "question": "At what point in the video does \"The lady picks up the box again\" happen? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_7",
+    "question": "At what time in the video does \"The forklift moves forward\" take place? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_0",
+    "question_id": "concat_wh_52_0.mp4_8",
+    "question": "At what point in the video does \"The forklift's arms go up to pick up boxes\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_300",
+    "question_id": "concat_wh_52_300.mp4_0",
+    "question": "At what point in the video does \"A forklift with boxes moves forward\" happen? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_300",
+    "question_id": "concat_wh_52_300.mp4_1",
+    "question": "At what point in the video does \"People cross in front of the forklift\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_300",
+    "question_id": "concat_wh_52_300.mp4_2",
+    "question": "At what point in the video does \"The forklift moves forward\" happen? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_300",
+    "question_id": "concat_wh_52_300.mp4_3",
+    "question": "When is \"The forklift's arms go up with boxes on top\" depicted in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_0",
+    "question": "When does \"A forklift removes boxes from the shelf\" happen in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_1",
+    "question": "When is \"People without uniforms are walking in the warehouse\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_2",
+    "question": "At what point in the video does \"A forklift with boxes on it moves forward\" happen? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_3",
+    "question": "When is \"There are boxes on the floor\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_4",
+    "question": "When does \"The forklift is stuck because of boxes on the floor\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_5",
+    "question": "When is \"People remove the boxes\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_910",
+    "question_id": "concat_wh_52_910.mp4_6",
+    "question": "At what point in the video does \"The forklift with boxes on it moves forward\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_0",
+    "question": "When does \"A worker wearing a protective vest and yellow hard hat walks forward and smiles\" happen in the video? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_1",
+    "question": "At what point in the video does \"A worker takes of his hat and puts it back on\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_2",
+    "question": "At what point in the video does \"A cart with boxes on it arrives\" happen? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_3",
+    "question": "When does \"Workers move boxes down a conveyor belt\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_4",
+    "question": "When is \"A box falls off the conveyor belt\" depicted in the video? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_5",
+    "question": "At what point in the video does \"A box falls off the conveyor belt\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_6",
+    "question": "At what time in the video does \"A box falls off the conveyor belt\" take place? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_7",
+    "question": "At what point in the video does \"Workers are chitchatting\" happen? Provide a response using only start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_8",
+    "question": "At what point in the video does \"A man in a white shirt without a uniform is holding his phone and walking\" happen? Convey your answer using start and end timestamps exclusively.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_1890",
+    "question_id": "concat_wh_52_1890.mp4_9",
+    "question": "At what point in the video does \"Two people wearing dark clothes and no uniform are walking and chatting\" happen? Answer the question only using start and end timestamps.",
+    "duration": 60.0
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_0",
+    "question": "At what point in the video does \"A robot carrying a box is still in a warehouse, and there are people on the sides\" happen? Provide a response using only start and end timestamps.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_1",
+    "question": "At what time in the video does \"A box falls off the robot\" take place? Answer the question only using start and end timestamps.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_2",
+    "question": "At what point in the video does \"A man wearing white without uniform walks closer to the camera\" happen? Provide a response using only start and end timestamps.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_3",
+    "question": "When does \"A man puts the box back on the robot\" happen in the video? Convey your answer using start and end timestamps exclusively.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_4",
+    "question": "At what time in the video does \"The man wearing white without uniform walks away\" take place? Answer the question only using start and end timestamps.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_5",
+    "question": "At what point in the video does \"A forklift moves closer to the camera\" happen? Provide a response using only start and end timestamps.",
+    "duration": 59.562
+  },
+  {
+    "vid": "concat_wh_52_2925",
+    "question_id": "concat_wh_52_2925.mp4_6",
+    "question": "When does \"A robot with a box is blocking the forklift\" happen in the video? Provide a response using only start and end timestamps.",
+    "duration": 59.562
+  }
+]

data/vqa/data_jsons/annotations/metrics_spatial_ss.json

@@ -0,0 +1,2636 @@
+[
+  {
+    "dimension": "Spatial reasoning",
+    "question": "How many people can exit the door at once while walking?",
+    "options": [
+      "4",
+      "all",
+      "3",
+      "2"
+    ],
+    "q_uid": "GX010071_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people went to the left and right after exiting the door?",
+    "options": [
+      "left-3 and right-9",
+      "left-4 and right-8",
+      "left-4 and right-9",
+      "left-3 and right-7"
+    ],
+    "q_uid": "GX010071_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people holding objects walk out of the room?",
+    "options": [
+      "5",
+      "12",
+      "6",
+      "4"
+    ],
+    "q_uid": "GX010071_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the outfit color of the last person running and entering the frame?",
+    "options": [
+      "White",
+      "Red",
+      "Black",
+      "Brown"
+    ],
+    "q_uid": "GX010031_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why do three men walk backward and stand on the frame?",
+    "options": [
+      "To see the vehicle entering the frame",
+      "To see the men running and entering the frame",
+      "To see the man adjusting the tripod stand",
+      "To see the men running and exiting the frame"
+    ],
+    "q_uid": "GX010031_Clip_5.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these items are not seen in the video segment?",
+    "options": [
+      "Tripod stands",
+      "Bag",
+      "Laptop",
+      "Digital camera"
+    ],
+    "q_uid": "GX010031_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people run and enter the frame?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010031_Clip_5.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Walking in the same orientation as shown in the video, how many people can walk through the transparent gate simultaneously?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In this scene, which way is the person moving on the stairs?",
+    "options": [
+      "Upwards",
+      "Downwards",
+      "Sideways",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Walking in the same orientation as shown in the scene, how many people can pass through the transparent gates simultaneously?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010032_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people pass through the transparent gates in the scene?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010032_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the outfit of the person walking through the frame and exiting in between two jumps happening in the segment?",
+    "options": [
+      "White half-sleeve t-shirt",
+      "White full-sleeve t-shirt",
+      "Brown full-sleeve t-shirt",
+      "Black shirt"
+    ],
+    "q_uid": "GX010013_Clip_5.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "Which of these items is present with the most number of people in the segment?",
+    "options": [
+      "Laptop",
+      "Coffe mug",
+      "Badge",
+      "Bag"
+    ],
+    "q_uid": "GX010013_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why are some people jumping in the scene?",
+    "options": [
+      "To avoid water on the ground",
+      "To avoid waste particles on the ground",
+      "To avoid the use of badge in check-in kiosk",
+      "To reach first in the office meeting"
+    ],
+    "q_uid": "GX010013_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people walk backward just before the man wearing a brown full-sleeve t-shirt jumps and enters the frame?",
+    "options": [
+      "None",
+      "One",
+      "Two",
+      "Three"
+    ],
+    "q_uid": "GX010013_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "A person entering a door while pulling a trolley",
+      "People entering a door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people enter the door on the left in the scene?",
+    "options": [
+      "Four",
+      "Five",
+      "Six",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What objects are present in the scene?",
+    "options": [
+      "Metal rods",
+      "Brown boxes",
+      "A plant",
+      "All of the above"
+    ],
+    "q_uid": "GX010013_Clip_3.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What was the reaction to the hand gesture made by the person wearing a white sweatshirt between times 1 and 2 seconds?",
+    "options": [
+      "A person started running in that direction",
+      "People started to look in that direction",
+      "People started to make the same gesture",
+      "No reaction was observed"
+    ],
+    "q_uid": "GX010013_Clip_1.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "Describe which option best describes what is happening in the scene.",
+    "options": [
+      "People are standing in a group, then they start running while another person joins them in running",
+      "People are standing in a group, then they start running",
+      "People are standing in a group, then they start climbing stairs",
+      "People are standing in a group, then they start walking"
+    ],
+    "q_uid": "GX010013_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the actions did not happen in the segment?",
+    "options": [
+      "People climb stairs",
+      "People run",
+      "People make hand gestures",
+      "People open their handbags"
+    ],
+    "q_uid": "GX010013_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people run during the segment?",
+    "options": [
+      "Eleven",
+      "Tweleve",
+      "Ten",
+      "Nine"
+    ],
+    "q_uid": "GX010013_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Describe the clothes of the person who starts running first.",
+    "options": [
+      "Blue jacket, black pants, and black t-shirt",
+      "White sweatshirt and black pants",
+      "Brown T-shirt and green pants",
+      "Orange sweatshirt and jeans"
+    ],
+    "q_uid": "GX010013_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people are walking in the opposite direction to that of people running?",
+    "options": [
+      "Three",
+      "Four",
+      "Five",
+      "Six"
+    ],
+    "q_uid": "GX010031_Clip_1.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People are standing in a group, then they start running while a person runs toward them",
+      "People are standing in a group doing handshakes, then they start running while a person runs towards them",
+      "People are standing in a group doing handshakes while a person runs towards them",
+      "People are standing in a group, then they start running while a person runs away from them"
+    ],
+    "q_uid": "GX010031_Clip_1.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "At 0 seconds, which combination of clothes is not visible?",
+    "options": [
+      "Orange sweatshirt and blue pants",
+      "White sweatshirt and black pants",
+      "Green t-shirt and black pants",
+      "Yellow sweatshirt and red pants"
+    ],
+    "q_uid": "GX010031_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "In the scene, how many people leap over the transparent gates?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People leaping over the transparent gates and running",
+      "People walking",
+      "People standing",
+      "All of the above"
+    ],
+    "q_uid": "GX010071_Clip_5.mp4"
+  },
+  {
+    "dimension": "Object reasoning",
+    "question": "What is the violation in the scene?",
+    "options": [
+      "People holding boxes",
+      "People pulling trolleys",
+      "People leaping over the turnstiles",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_5.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What do all three men have in common?",
+    "options": [
+      "Same color clothes",
+      "Watches",
+      "ID cards on their waists",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "What strange behavior is taking place among the people there?",
+    "options": [
+      "People walking",
+      "People pushing each other",
+      "People standing",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_7.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "In the scene, what object are the people passing by holding?",
+    "options": [
+      "Bottles",
+      "Mobile phones",
+      "Bags",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People standing",
+      "People walking by",
+      "People pushing each other",
+      "All of the above"
+    ],
+    "q_uid": "GX010013_Clip_7.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People are repairing a glass turnstile",
+      "People carrying objects walk through glass turnstile multiple times",
+      "People are throwing objects",
+      "People are pushing the doors open"
+    ],
+    "q_uid": "GX010072_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the outfit of the man who tailgates another man having a badge?",
+    "options": [
+      "White full-sleeve t-shirt and black pants",
+      "White full-sleeve t-shirt and blue pants",
+      "Black full-sleeve t-shirt and black pants",
+      "Black full-sleeve t-shirt and white pants"
+    ],
+    "q_uid": "GX010072_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Which direction was the man wearing a white full-sleeve t-shirt walking when he showed a badge?",
+    "options": [
+      "Away from the frame",
+      "Towards the frame",
+      "Towards the stairs",
+      "None of the above"
+    ],
+    "q_uid": "GX010072_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many times is the glass turnstile door opened without showing a badge?",
+    "options": [
+      "Three",
+      "Two",
+      "One",
+      "None"
+    ],
+    "q_uid": "GX010072_Clip_2.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "Describe the attire of the person wearing bag in front of them.",
+    "options": [
+      "Green t-shirt, black pants, and a pair of spectacles",
+      "Black hoodie, blue jeans, and a pair of spectacles",
+      "Black sweater, white T-shirt, and black pants",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_4.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "In the scene, what items are some of the individuals holding?",
+    "options": [
+      "Laptops",
+      "paper cups",
+      "Sheets of paper",
+      "All of the above"
+    ],
+    "q_uid": "GX010013_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are walking in the scene?",
+    "options": [
+      "Eleven",
+      "Twelve",
+      "Thirteen",
+      "None of the above"
+    ],
+    "q_uid": "GX010013_Clip_4.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction are the people walking in the scene?",
+    "options": [
+      "Towards the right side of the camera view",
+      "Towards the camera",
+      "Towards the left side of the camera view",
+      "All of the above"
+    ],
+    "q_uid": "GX010013_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "In the scene, why do people scan cards beside the transparent gates multiple times?",
+    "options": [
+      "To open the gates",
+      "To close the gates",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "How many people can pass through a single turnstile simultaneously?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Which direction are people walking on the stairs in the scene?",
+    "options": [
+      "upwards",
+      "downwards",
+      "both a and b",
+      "none of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "In the scene, what objects are there on the trolley?",
+    "options": [
+      "A brown box",
+      "A black cabinet",
+      "Laptops",
+      "All of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people pass through the transparent gates?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People passing through the transparent gates while holding boxes",
+      "People walking down the stairs",
+      "A man pushing a trolley",
+      "All of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people in the segment are holding an object in their hand?",
+    "options": [
+      "2",
+      "3",
+      "4",
+      "1"
+    ],
+    "q_uid": "GX010014_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people walked through the path behind the plant in this segment?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010071_Clip_2.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "In which direction did the man wearing a suit walk after interacting with another man wearing a red shirt?",
+    "options": [
+      "Towards the right side of the man wearing the suit",
+      "Towards the left side of the man wearing the suit",
+      "Turns and walks backwards",
+      "Towards the crowd"
+    ],
+    "q_uid": "GX010071_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Why did the man wearing a bag run through the left side of the pillar?",
+    "options": [
+      "To easily enter the room on the left",
+      "Right side was congested, whereas left side was free",
+      "To talk to the man standing on the left side",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "What caused the group of people to run immediately?",
+    "options": [
+      "A fire broke out in the building",
+      "A man fired gunshots",
+      "A man runs and enters the frame from behind",
+      "A sudden earthquake hit the area"
+    ],
+    "q_uid": "GX010071_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object reasoning",
+    "question": "What is the violation in the segment?",
+    "options": [
+      "creating distraction",
+      "touching hands",
+      "friendly banter",
+      "pushing each other"
+    ],
+    "q_uid": "GX010031_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What happened before the trolley with the dustbin appeared partially?",
+    "options": [
+      "The trolley came first, and then people started pushing.",
+      "A person pushes a man, a person walks across, and people are speaking.",
+      "A person walked across, then the trolley appeared.",
+      "The trolley was always visible."
+    ],
+    "q_uid": "GX010031_Clip_6.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "What was happening in the scene when a person walked across?",
+    "options": [
+      "two people were pushing each other",
+      "a group of people were speaking",
+      "another person was walking",
+      "people were standing without interaction"
+    ],
+    "q_uid": "GX010031_Clip_6.mp4"
+  },
+  {
+    "dimension": "Object reasoning",
+    "question": "What is the use of the vehicle seen in the segment?",
+    "options": [
+      "For transportation of people",
+      "For transportation of goods",
+      "For cleaning purpose",
+      "For advertisement"
+    ],
+    "q_uid": "GX010013_Clip_6.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "Which direction did the man wearing a backpack walk?",
+    "options": [
+      "Towards the staircase",
+      "Towards the elevator",
+      "Towards the check-in kiosk",
+      "Towards the room"
+    ],
+    "q_uid": "GX010013_Clip_6.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these items are not seen in the video?",
+    "options": [
+      "Backpack",
+      "Laptop",
+      "Watch",
+      "Chair"
+    ],
+    "q_uid": "GX010013_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of these actions were not performed by the man in the white outfit and the man in the black outfit who are facing each other?",
+    "options": [
+      "Pushing each other",
+      "Talking each other",
+      "Walking backwards",
+      "Shake their hands"
+    ],
+    "q_uid": "GX010013_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why do people scan cards multiple times beside the transparent gates?",
+    "options": [
+      "To open the gate",
+      "To close the gate",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010032_Clip_1.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What objects are there on the trolley in the scene?",
+    "options": [
+      "A brown box",
+      "Laptops",
+      "A black cabinet",
+      "All of the above"
+    ],
+    "q_uid": "GX010032_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People walking while holding brown boxes",
+      "A man pushing a trolley",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010032_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are visible holding objects in their hands in the segment?",
+    "options": [
+      "8",
+      "1",
+      "2",
+      "3"
+    ],
+    "q_uid": "GX010031_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "A group of people is pushing each other; a man and a woman walk across.",
+      "A group of people is pushing each other; a man and a woman walk across with objects in their hands.",
+      "A group of people is pushing each other",
+      "A group of people is pushing each other, and a man walks with a laptop in his hand."
+    ],
+    "q_uid": "GX010031_Clip_7.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people enter the room in the segment?",
+    "options": [
+      "12",
+      "6",
+      "5",
+      "4"
+    ],
+    "q_uid": "GX010071_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the activities were not performed in the segment?",
+    "options": [
+      "a person opened a small door",
+      "People are walking",
+      "people are going up the stairs",
+      "people do handshakes"
+    ],
+    "q_uid": "GX010071_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are visible in the segment?",
+    "options": [
+      "16",
+      "12",
+      "10",
+      "11"
+    ],
+    "q_uid": "GX010071_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these objects are not visible in the scene?",
+    "options": [
+      "Plant",
+      "Boxes",
+      "Shoulder bags",
+      "Laptops"
+    ],
+    "q_uid": "GX010013_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of these actions is happening in the scene?",
+    "options": [
+      "People are running, walking, and making gestures",
+      "People are running and making gestures",
+      "People are walking and making gestures",
+      "People are running and jumping"
+    ],
+    "q_uid": "GX010013_Clip_2.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "Describe what the person who starts running first is wearing.",
+    "options": [
+      "Green T-shirt and black pants",
+      "White sweatshirt and black pants",
+      "Grey T-shirt and blue pants",
+      "Brown sweatshirt and green pants"
+    ],
+    "q_uid": "GX010013_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Which space is utilized by more people to walk in the frame?",
+    "options": [
+      "Through the left side of the leftmost tripod stand",
+      "Through the right side of the rightmost tripod stand",
+      "Through the center of both tripod stands",
+      "None of the above"
+    ],
+    "q_uid": "GX010031_Clip_4.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People are talking each other",
+      "People are walking",
+      "People are dancing",
+      "A man is opening a door"
+    ],
+    "q_uid": "GX010031_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people wearing bags are walking through the frame?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010031_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people with bags are standing in the group at 4 seconds?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "Five"
+    ],
+    "q_uid": "GX010031_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What did the people do after 13 seconds?",
+    "options": [
+      "Walking and standing",
+      "Running",
+      "Standing",
+      "Walking"
+    ],
+    "q_uid": "GX010031_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which person started running at the last of the given options?",
+    "options": [
+      "The person wearing an orange sweatshirt and blue pants",
+      "The person wearing a grey t-shirt and blue pants",
+      "The person wearing a brown sweatshirt and green pants",
+      "The person wearing white sweatshirt and black pants"
+    ],
+    "q_uid": "GX010031_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Will the brown box at the top fit inside the brown box at the bottom?",
+    "options": [
+      "Yes, one such box will fit",
+      "Yes, two such boxes will fit",
+      "Yes, three such boxes will fit",
+      "No, they are the same size"
+    ],
+    "q_uid": "GX010031_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Why did the person at 4 seconds walk towards a different path from where a group of people are standing?",
+    "options": [
+      "He saw a sign to avoid the path",
+      "There was no space in that path",
+      "A person made gestures for him to go on that path",
+      "There was no apparent reason"
+    ],
+    "q_uid": "GX010031_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What caused one of the glass turnstile doors to open for some time?",
+    "options": [
+      "A man pushed the door",
+      "A man pulled the door",
+      "A man waved his hand at the switch",
+      "A man swiped a badge"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "What is the direction in which the woman carrying a bag turns after climbing down the stairs?",
+    "options": [
+      "Towards her left side",
+      "Towards her right side",
+      "Turns backwards",
+      "None of the above"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "What caused the man with the trolley to wait in front of the glass turnstile door?",
+    "options": [
+      "Another person was standing in front of him",
+      "His trolley carrying larger boxes got stuck in the door",
+      "A man was repairing the glass turnstile",
+      "Door was not opened and required assistance from opposite side"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why did the man wearing a black full-sleeve t-shirt tailgate another man?",
+    "options": [
+      "He did not have a badge",
+      "He was talking to the man walking infront of him",
+      "Glass turnstile did not open when he showed his badge",
+      "To handover an object to the man in front"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people are climbing up the stairs in this segment?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What type of boxes are carried by the men?",
+    "options": [
+      "Identical boxes",
+      "Open boxes",
+      "Different size boxes",
+      "Transparent boxes"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "A few men repairing the glass turnstile",
+      "A few men carrying boxes are passing through glass turnstile",
+      "A few men keeps the boxes in a corner",
+      "A few men pick up boxes from a corner"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "How was the glass turnstile functioned when the first person went through it?",
+    "options": [
+      "Manually by an operator",
+      "Using automatic sensors",
+      "By pressing a button",
+      "By showing badges"
+    ],
+    "q_uid": "GX010072_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the violation here?",
+    "options": [
+      "walking downstairs",
+      "kicking each other",
+      "pushing each other",
+      "punching on face"
+    ],
+    "q_uid": "GX010071_Clip_7.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction is the person on the stairs moving?",
+    "options": [
+      "upward",
+      "to left",
+      "to right",
+      "downward"
+    ],
+    "q_uid": "GX010071_Clip_7.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction did the man in the black hoodie get pushed by two other individuals?",
+    "options": [
+      "towards the man wearing white",
+      "towards the stairs",
+      "towards the gates",
+      "both a and b"
+    ],
+    "q_uid": "GX010071_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "people walking",
+      "people pushing each other",
+      "a person walking downstairs",
+      "all of the above"
+    ],
+    "q_uid": "GX010071_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "What is the abnormal behavior of people seen in this segment?",
+    "options": [
+      "They stand in a group",
+      "Some of them are walking",
+      "Started running immediately",
+      "Pushes each other"
+    ],
+    "q_uid": "GX010071_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of these activities is not happening in the scene?",
+    "options": [
+      "People walking",
+      "People running",
+      "People speaking",
+      "People climbing steps"
+    ],
+    "q_uid": "GX010071_Clip_1.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "How many people run through the left side of the pillar?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None"
+    ],
+    "q_uid": "GX010071_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the exact timestamp at which a man wearing a bag runs and enters the frame?",
+    "options": [
+      "4th second",
+      "5th second",
+      "6th second",
+      "7th second"
+    ],
+    "q_uid": "GX010071_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the actions were not performed in the segment?",
+    "options": [
+      "a person opens a door",
+      "people walking",
+      "a person closes a door",
+      "stopping a person from opening a door"
+    ],
+    "q_uid": "GX010031_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people enter the two rooms in the segment?",
+    "options": [
+      "7",
+      "6",
+      "9",
+      "8"
+    ],
+    "q_uid": "GX010031_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What is the person wearing a gray t-shirt holding in his hands?",
+    "options": [
+      "Mobile phone",
+      "ATM card",
+      "Pen",
+      "Badge"
+    ],
+    "q_uid": "vqa_172a5f65d7.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many CCTV cameras are placed above the door?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "vqa_172a5f65d7.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why is the man wearing a red striped t-shirt walking quickly towards the door?",
+    "options": [
+      "The man is exercising",
+      "The man follows and talks to the man wearing gray t-shirt",
+      "The man doesn't have a badge and enters the building without a badge",
+      "It is raining outside and the man is hurrying to get inside"
+    ],
+    "q_uid": "vqa_172a5f65d7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Why was the door not opened when the man wearing a gray t-shirt pushed it initially?",
+    "options": [
+      "Not enough force was exerted",
+      "It was locked",
+      "Sensor complaint",
+      "Opposite action was performed by the man"
+    ],
+    "q_uid": "vqa_172a5f65d7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What do the two people entering through the door have in common in the scene?",
+    "options": [
+      "Spectacles",
+      "Scanning cards beside the door and entering the door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_9.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which person entered through the door without scanning a card next to it in the scene?",
+    "options": [
+      "First person",
+      "Third person",
+      "Last person",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_9.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people enter through the door in the scene?",
+    "options": [
+      "3",
+      "4",
+      "5",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_9.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "How many people entering the room are not wearing black shirts/t-shirts?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010069_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these items are not seen in the video segment?",
+    "options": [
+      "Watch",
+      "Tripod stand",
+      "Badge",
+      "Laptop"
+    ],
+    "q_uid": "GX010069_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How did the man in the green t-shirt open the door?",
+    "options": [
+      "The door was not locked",
+      "The man pushed the door with extreme force",
+      "A person assisted him from inside the room",
+      "He showed the badge infront of the scanner before turning the handle"
+    ],
+    "q_uid": "GX010069_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "In which direction did the last person who walked through the corridor move?",
+    "options": [
+      "Towards his left",
+      "Towards his right",
+      "Straight towards the corner",
+      "Stops at the corner and turns back"
+    ],
+    "q_uid": "GX010069_Clip_3.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction did the individual walk?",
+    "options": [
+      "Towards the door",
+      "Towards the camera",
+      "Towards the metal rod",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are walking in the hallway?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many individuals walk through the door on the right?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction did the last two individuals walk?",
+    "options": [
+      "Towards the camera",
+      "Toward the door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are wearing glasses?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people walk through the white door that is on the right side?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In the scene, which direction are people walking on the stairs?",
+    "options": [
+      "Upwards",
+      "Downwards",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people pass through the turnstiles?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "In the scene, what objects are being held by the people passing through the turnstiles?",
+    "options": [
+      "Mobile phones",
+      "Laptops",
+      "Sheets of paper",
+      "All of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In a single turnstile, how many people could go through at once?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People walking",
+      "People standing",
+      "A woman walking down stairs",
+      "All of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the man in the black outfit doing in the scene?",
+    "options": [
+      "dragging a trolley that has a paper on it.",
+      "dragging the trolley that has a laptop on it.",
+      "dragging the trolley that has a CPU on it.",
+      "dragging the trolley that has a PC, paper, and CPU on it."
+    ],
+    "q_uid": "GX010069_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What happened after the door opened?",
+    "options": [
+      "people are walking inside one by one",
+      "people are running",
+      "a woman walked inside",
+      "people are walking out one by one"
+    ],
+    "q_uid": "GX010029_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are entering the room without punching the card?",
+    "options": [
+      "3",
+      "5",
+      "4",
+      "6"
+    ],
+    "q_uid": "GX010029_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people pass the automatic door system?",
+    "options": [
+      "5",
+      "6",
+      "7",
+      "8"
+    ],
+    "q_uid": "GX010030_Clip_5.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are wearing glasses in this scene?",
+    "options": [
+      "2",
+      "1",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010029_Clip_10.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What object are the people not holding in this scene?",
+    "options": [
+      "Monitor",
+      "Clipboard",
+      "Folded chair",
+      "Bottle"
+    ],
+    "q_uid": "GX010029_Clip_10.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "What direction does the first person exiting the room walk towards?",
+    "options": [
+      "Right",
+      "Straight",
+      "Left",
+      "The person stands still"
+    ],
+    "q_uid": "GX010029_Clip_10.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people come out of the door?",
+    "options": [
+      "2",
+      "1",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010029_Clip_10.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What is the person wearing white shoes and a blue top crossing the gates carrying?",
+    "options": [
+      "A backpack",
+      "A fanny pack",
+      "A laptop",
+      "A paper"
+    ],
+    "q_uid": "GX010012_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people go down the stairs holding a cup?",
+    "options": [
+      "1",
+      "3",
+      "2",
+      "0"
+    ],
+    "q_uid": "GX010012_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What happens after the 9th second?",
+    "options": [
+      "A man in a green T-shirts runs a little bit",
+      "A man in a white T-shirt walks ahead",
+      "The gates open for the man in a white T-shirt",
+      "A person goes up the stairs"
+    ],
+    "q_uid": "GX010012_Clip_3.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Can two people fit between the gates at once to get through?",
+    "options": [
+      "Yes",
+      "No"
+    ],
+    "q_uid": "GX010012_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people hold cards at the gate to get through it?",
+    "options": [
+      "2",
+      "1",
+      "4",
+      "3"
+    ],
+    "q_uid": "GX010012_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "In the scene, where did the man wearing a green shirt put his phone before leaving?",
+    "options": [
+      "In his pant pocket",
+      "In a blue bag hanging around his shoulders",
+      "In his shirt pocket",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_8.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "How many people can enter the white door at once while entering in the same orientation as those entering?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_8.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People walking",
+      "People knocking on a door",
+      "People opening and walking through a door",
+      "All of the above"
+    ],
+    "q_uid": "GX010069_Clip_8.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "What's the total number of people that walk in the three lanes?",
+    "options": [
+      "4",
+      "6",
+      "5",
+      "7"
+    ],
+    "q_uid": "GX010012_Clip_5.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the color of the man's t-shirt that's walking in the middle lane with a bag?",
+    "options": [
+      "Black",
+      "Green",
+      "Red",
+      "Gray"
+    ],
+    "q_uid": "GX010012_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Why is the man in a white outfit carrying an object with one hand when he re-enters the hallway?",
+    "options": [
+      "The man is helping another man with his other hand.",
+      "The object is small in size and can be carried with one hand.",
+      "The man has to keep his other hand free to high-five.",
+      "The man is holding another object in his other hand."
+    ],
+    "q_uid": "GX010069_Clip_11.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why is the man opening the door with his leg?",
+    "options": [
+      "The man is disabled.",
+      "The man is holding a child.",
+      "The man is holding an object with both hands.",
+      "The man's hands are dirty."
+    ],
+    "q_uid": "GX010069_Clip_11.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people went left, right, and center after passing the system?",
+    "options": [
+      "left-2, right-2, center-1",
+      "left-1, right-1, center-3",
+      "left-1, right-2, center-2",
+      "left-3 and right-2, center-1"
+    ],
+    "q_uid": "GX010070_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people fold their hands at least once during this segment?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "none of the above"
+    ],
+    "q_uid": "GX010070_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the activities were not performed in the video?",
+    "options": [
+      "people walking",
+      "people speaking to each other",
+      "people standing next to each other",
+      "people doing handshakes"
+    ],
+    "q_uid": "GX010070_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why did the man wearing a green t-shirt wait outside the room?",
+    "options": [
+      "Waited for permission",
+      "He forgot the key",
+      "The room was crowded",
+      "Waited for others to enter"
+    ],
+    "q_uid": "GX010069_Clip_1.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the outfit color of the fifth person entering the room?",
+    "options": [
+      "Black",
+      "Green",
+      "White",
+      "Brown"
+    ],
+    "q_uid": "GX010069_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of these actions were not performed by the man wearing a green t-shirt after approaching the door?",
+    "options": [
+      "Shows a badge infront of the scanner",
+      "Turns the door handle",
+      "Pulls the door",
+      "Pushes the door"
+    ],
+    "q_uid": "GX010069_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people wearing bags entered the room?",
+    "options": [
+      "Eleven",
+      "Four",
+      "Three",
+      "Two"
+    ],
+    "q_uid": "GX010069_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "What is the ratio of women to men coming out of a room?",
+    "options": [
+      "11:1",
+      "1:11",
+      "1:10",
+      "10:1"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "A man in glasses and a black jacket comes out of the room at tenth position what is he carrying in his hands?",
+    "options": [
+      "Notebook in both the hands",
+      "A notebook in the right hand and a device in the left hand",
+      "A notebook in the left hand and a device in the right hand",
+      "Just a notebook in the left hand."
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "At what timestamp does the man in white upper wear and a black pant opens the door of another room?",
+    "options": [
+      "20 seconds",
+      "33 seconds",
+      "40 seconds",
+      "48 seconds"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Is there any pattern the people are following while entering another room?",
+    "options": [
+      "Yes, firstly a man enters and then a woman.",
+      "Yes, people are entering the room alternatively.",
+      "No one enters into another room.",
+      "No, they are randomly entering the room."
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Are the people interacting with each other while walking?",
+    "options": [
+      "Yes",
+      "No"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "After how many men does a woman exit the room?",
+    "options": [
+      "10",
+      "3",
+      "6",
+      "2"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "Describe the person who opens the white door.",
+    "options": [
+      "A man in a green T-shirt and a white pant with a hat",
+      "A man in a black pant and blue T-shirt",
+      "A man in a green t-shirt and a black pant wearing glasses",
+      "A man carrying a bag in a blue outfit"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are coming out of a room?",
+    "options": [
+      "11",
+      "15",
+      "20",
+      "18"
+    ],
+    "q_uid": "GX010029_Clip_2.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "How many people can walk out of the door simultaneously if they walk the same as shown in the scene?",
+    "options": [
+      "0",
+      "1",
+      "2",
+      "Can't be determined"
+    ],
+    "q_uid": "GX010069_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "Three men are walking out of the room.",
+      "Three men walked out of the room, with only one man with empty hands.",
+      "Three men walked out of the room while all holding boxes in their hands.",
+      "Three men walked out of the room while only two men were holding the boxes."
+    ],
+    "q_uid": "GX010069_Clip_5.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people walk out of the room while holding a box in their hand?",
+    "options": [
+      "0",
+      "2",
+      "1",
+      "3"
+    ],
+    "q_uid": "GX010069_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Is the person going downstairs with their right hand on the brown railing?",
+    "options": [
+      "Yes",
+      "There is no railing",
+      "No",
+      "Their hand is on the railing, half way down the stairs"
+    ],
+    "q_uid": "GX010012_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people's full body is not visible throughout the segment?",
+    "options": [
+      "1",
+      "2",
+      "7",
+      "3"
+    ],
+    "q_uid": "GX010012_Clip_4.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Does any person walk in the direction of the camera when passing through the gates?",
+    "options": [
+      "Yes, one person",
+      "Yes, two people",
+      "Yes, three people",
+      "No, no one"
+    ],
+    "q_uid": "GX010030_Clip_3.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "Describe what is happening in the scene",
+    "options": [
+      "People are walking through the gates, then they go and stand",
+      "People are walking through the gates",
+      "People are standing in a group",
+      "People are walking through the gates, then they climb stairs and stand"
+    ],
+    "q_uid": "GX010030_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these objects are not visible in the scene?",
+    "options": [
+      "Plant",
+      "Papers",
+      "Bag",
+      "Water bottle"
+    ],
+    "q_uid": "GX010030_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "What is the largest number of people who walk through the gate without it closing?",
+    "options": [
+      "Two",
+      "Four",
+      "Three",
+      "One"
+    ],
+    "q_uid": "GX010030_Clip_3.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of these objects is not visible?",
+    "options": [
+      "Laptop",
+      "Suitcase",
+      "Hand watch",
+      "Speakers"
+    ],
+    "q_uid": "GX010070_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Which law was violated in this scene?",
+    "options": [
+      "People walked through gates without scanning anything",
+      "People were forcefully opening closed gates",
+      "People were jumping over closed gates",
+      "No rules were broken"
+    ],
+    "q_uid": "GX010070_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people walked through the gates without scanning anything?",
+    "options": [
+      "seven",
+      "six",
+      "five",
+      "four"
+    ],
+    "q_uid": "GX010070_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of these actions are not happening in the scene?",
+    "options": [
+      "People are scanning their cards",
+      "Running",
+      "Climbing stairs",
+      "People are closing doors"
+    ],
+    "q_uid": "GX010070_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people come out from the left side that has the brown wall?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "0"
+    ],
+    "q_uid": "GX010070_Clip_1.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What kind of bag is the man in the green t-shirt carrying?",
+    "options": [
+      "Backpack",
+      "Suitcase",
+      "Handbag",
+      "Slingbag"
+    ],
+    "q_uid": "GX010070_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people in total walk through the lanes?",
+    "options": [
+      "4",
+      "5",
+      "6",
+      "7"
+    ],
+    "q_uid": "GX010070_Clip_1.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What is the object in the hand of the person in the video segment who is going through the right transparent gate?",
+    "options": [
+      "A mobile phone",
+      "A bag",
+      "A jacket",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "In the video segment, why did the man in the black jacket ask the woman in the pink outfit to pass through a different transparent gate?",
+    "options": [
+      "One of the transparent gates was not working",
+      "There is crowd at one transparent gate",
+      "Only one person at a time can pass through a transparent gate",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_1.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the color of the object that the man in the white T-shirt is carrying?",
+    "options": [
+      "Silver",
+      "Black",
+      "Red",
+      "Gold"
+    ],
+    "q_uid": "GX010029_Clip_11.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are carrying objects and walking?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010029_Clip_11.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are carrying brown boxes in the segment?",
+    "options": [
+      "3",
+      "2",
+      "1",
+      "4"
+    ],
+    "q_uid": "GX010070_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What is the eighth person going through the gate holding in their hand?",
+    "options": [
+      "A bag",
+      "A card",
+      "Papers",
+      "Books"
+    ],
+    "q_uid": "GX010070_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many gates are there through which people pass by?",
+    "options": [
+      "1",
+      "3",
+      "5",
+      "4"
+    ],
+    "q_uid": "GX010070_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many of the individuals were wearing spectacles?",
+    "options": [
+      "5",
+      "7",
+      "4",
+      "6"
+    ],
+    "q_uid": "GX010069_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Did the 10th person turn to their left after coming out of the white door?",
+    "options": [
+      "Yes",
+      "No"
+    ],
+    "q_uid": "GX010069_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people in total walked out of the room?",
+    "options": [
+      "5",
+      "11",
+      "9",
+      "10"
+    ],
+    "q_uid": "GX010069_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which items are shown in the video?",
+    "options": [
+      "Metal rods",
+      "Brown boxes",
+      "A white desk",
+      "All of the above"
+    ],
+    "q_uid": "GX010011_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people are wearing spectacles?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_3.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many persons walk through the hallway?",
+    "options": [
+      "3",
+      "4",
+      "5",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_3.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "What happened to the exterior silver knob of the door when the door opened from the inside?",
+    "options": [
+      "It turned to its right",
+      "A green light blinked from it",
+      "It remained still",
+      "It turned to its left"
+    ],
+    "q_uid": "GX010011_Clip_5.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "Could all three of the men walk side-by-side together through the corridor?",
+    "options": [
+      "Yes",
+      "No",
+      "Maybe",
+      "Uncertain"
+    ],
+    "q_uid": "GX010011_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the actions were not performed in the segment?",
+    "options": [
+      "knocking on the door",
+      "looking at the phone",
+      "speaking to each other",
+      "shaking their hands"
+    ],
+    "q_uid": "GX010029_Clip_8.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of the items did it not see in the video?",
+    "options": [
+      "watch",
+      "paper",
+      "phone",
+      "wallet"
+    ],
+    "q_uid": "GX010029_Clip_8.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the activities were not performed in the video?",
+    "options": [
+      "Walking",
+      "People greeting others",
+      "People scanning their card",
+      "People do handshakes"
+    ],
+    "q_uid": "GX010011_Clip_1.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "Describe what the second last person to enter the room is wearing.",
+    "options": [
+      "Black jacket, t-shirt, pants, and white shoes",
+      "White sweatshirt and black pants and shoes",
+      "Lime T-shirt and black shoes and pants",
+      "Green T-shirt, blue jeans, and black shoes"
+    ],
+    "q_uid": "GX010011_Clip_1.mp4"
+  },
+  {
+    "dimension": "Scene perception",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People are walking in a room while a person holds the door open",
+      "People are walking in a room and sitting in chairs",
+      "People are exiting a building",
+      "People are walking in a room and standing, while a person is standing at the door"
+    ],
+    "q_uid": "GX010011_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Identify which person changes lanes during the video segment.",
+    "options": [
+      "The driver",
+      "The passenger",
+      "The cyclist",
+      "The pedestrian"
+    ],
+    "q_uid": "GX010070.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "Among all the transparent gateways, identify the one with the highest number of people passing through.",
+    "options": [
+      "3",
+      "2",
+      "1",
+      "4"
+    ],
+    "q_uid": "GX010070.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "Four people carrying boxes are walking",
+      "Four people are talking each other",
+      "Three people carrying boxes are walking",
+      "Three people are talking each other"
+    ],
+    "q_uid": "GX010029_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What happens from the 9th second to the 10th second?",
+    "options": [
+      "A man wearing white outfit walks out of the room and closes the door",
+      "A man wearing black outfit walks out of the room",
+      "A man wearing white outfit walks through the corridor",
+      "A man wearing black outfit opens the door and walks out of the room"
+    ],
+    "q_uid": "GX010029_Clip_5.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the outfit color of the man carrying a box wrapped with blue packing tape on the sides?",
+    "options": [
+      "White",
+      "Green",
+      "Brown",
+      "Black"
+    ],
+    "q_uid": "GX010029_Clip_5.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many open boxes are carried by the men?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None"
+    ],
+    "q_uid": "GX010029_Clip_5.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the person doing in the video?",
+    "options": [
+      "Pulling a trolley",
+      "Opening a door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_7.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "What caused the trolley to be momentarily blocked while entering the door?",
+    "options": [
+      "A person stopped pulling the door",
+      "Because the width of the trolley and the door are comparable, the trolley must be rotated to fit through the door",
+      "An object blocking the door",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_7.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What happened after the door opened?",
+    "options": [
+      "People were walking out of the door one by one.",
+      "People were running.",
+      "People were sitting and watching the door.",
+      "None of the above."
+    ],
+    "q_uid": "GX010011_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people went to the left and right after exiting the door?",
+    "options": [
+      "left - 9 and right - 2",
+      "left - 5 and right - 4",
+      "left - 7 and right - 3",
+      "none of the above"
+    ],
+    "q_uid": "GX010011_Clip_2.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "Which of the actions were not performed in the segment?",
+    "options": [
+      "A person gives another person some papers",
+      "A person lifts object",
+      "A person opens a door",
+      "A person closes a door"
+    ],
+    "q_uid": "GX010011_Clip_10.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "What directions did the people go after exiting the room?",
+    "options": [
+      "The man with a black box went away from the camera, while the man with a monitor went towards it",
+      "The man with a black box went towards the camera while the man with a monitor went away from it",
+      "They both went towards the camera",
+      "They both went away from the camera"
+    ],
+    "q_uid": "GX010011_Clip_10.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "How was the door opened?",
+    "options": [
+      "A man pushed it",
+      "The door has sensors",
+      "A man scans on a black device",
+      "It was already opened"
+    ],
+    "q_uid": "GX010069_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In what direction does the third man in the scene turn to enter a room?",
+    "options": [
+      "left, but the viewer's right",
+      "Right",
+      "North",
+      "South"
+    ],
+    "q_uid": "GX010069_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people enter the room?",
+    "options": [
+      "5",
+      "4",
+      "3",
+      "6"
+    ],
+    "q_uid": "GX010069_Clip_6.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many individuals in total enter the room in the segment?",
+    "options": [
+      "Four individuals",
+      "Five individuals",
+      "Six individuals",
+      "Seven individuals"
+    ],
+    "q_uid": "GX010069_Clip_9.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why are the individuals seen pushing cards against the black rectangular object on the white wall in the segment?",
+    "options": [
+      "They authenticate their cards for unlocking the door and enter it",
+      "They push the card to make the white wall attractive",
+      "They push the card to change the color of the door knob",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_9.mp4"
+  },
+  {
+    "dimension": "OCR problems",
+    "question": "What are the alphanumeric symbols on the square white panel above the rectangular black object on the white wall in the segment?",
+    "options": [
+      "L1-5052",
+      "L1-E032",
+      "L1-E0E2",
+      "L1-3032"
+    ],
+    "q_uid": "GX010069_Clip_9.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "What lanes allow access for the individuals to pass through?",
+    "options": [
+      "The first, second, and third lanes from the left side",
+      "The second, third, and fourth lanes from the left side with green lights",
+      "The third, fourth, and fifth lanes from the left side",
+      "None of the above"
+    ],
+    "q_uid": "GX010012_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What are the three individuals seen carrying down the staircase?",
+    "options": [
+      "(A) Yellow boxes and a suitcase",
+      "(B) Green trolleys and a camera",
+      "(C) Red boxes and a camera",
+      "(D) Purple boxes and a suitcase"
+    ],
+    "q_uid": "GX010012_Clip_2.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "What object does the woman entering through the third lane hold in her hand?",
+    "options": [
+      "A television",
+      "A partially folded laptop",
+      "A book",
+      "A bag"
+    ],
+    "q_uid": "GX010012_Clip_2.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many individuals walk down the stairs from the timestamp of 1 second to the timestamp of 13 seconds within the specified time segment?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010012_Clip_2.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the outfit color of the man walking through the corridor at last?",
+    "options": [
+      "White",
+      "Blue",
+      "Red",
+      "Black"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Which of these directions have the men in the segment not taken?",
+    "options": [
+      "Turns left and enters the room",
+      "Walks straight in the corridor",
+      "Turns left and leaves the corridor",
+      "Turns right and leaves the corridor"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many times was the door closed in the segment?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why did two men walk through the corridor?",
+    "options": [
+      "As a workout exercise",
+      "To talk each other",
+      "To enter a room",
+      "To leave the building"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What did the man wearing a red long-sleeved t-shirt do initially after approaching to the door?",
+    "options": [
+      "Pushed the door",
+      "Insert the key on the door",
+      "Turns the door handle",
+      "Shows a badge in front of a scanner"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "How many people walked and crossed the man wearing a green t-shirt?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010011_Clip_6.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What did the man in the black outfit do after stopping in front of the closed white door?",
+    "options": [
+      "He knocked on the door with his hand.",
+      "He placed a card on a black device attached beside the door on the wall.",
+      "He pressed the bell on the wall.",
+      "He turned the knob on the door and opened it."
+    ],
+    "q_uid": "GX010011_Clip_7.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What objects are kept on the silver trolley?",
+    "options": [
+      "A black monitor screen and a black CPU",
+      "Two black cabinets",
+      "Two black boxes",
+      "A black speaker and a monitor screen"
+    ],
+    "q_uid": "GX010011_Clip_7.mp4"
+  },
+  {
+    "dimension": "Spatial reasoning",
+    "question": "How many people, walking in the same orientations as the scene, could fit through the transparent door at once?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people passed through the transparent doors?",
+    "options": [
+      "3",
+      "4",
+      "5",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is happening in the scene?",
+    "options": [
+      "People standing",
+      "People walking through the transparent doors",
+      "People walking down the stairs",
+      "All of the above"
+    ],
+    "q_uid": "GX010030_Clip_4.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the man in the red shirt doing throughout the scene?",
+    "options": [
+      "Walking, standing, and using gestures",
+      "Walking, standing, and speaking",
+      "Walking",
+      "Standing and speaking"
+    ],
+    "q_uid": "GX010011_Clip_8.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the man in the white t-shirt doing throughout the scene?",
+    "options": [
+      "Knocking on the door, making hand gestures, standing, and speaking.",
+      "Knocking on the door and walking around.",
+      "Knocking on the door, making hand gestures, standing, speaking, and closing the door.",
+      "Speaking, standing, and making gestures."
+    ],
+    "q_uid": "GX010011_Clip_8.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many individuals in black outfits go through the door?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010029_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What did the man wearing a green t-shirt do at the end?",
+    "options": [
+      "Closed the door",
+      "Opened the door",
+      "Follows the man wearing black t-shirt and enters the room",
+      "Follows the man wearing white full-sleeve t-shirt and enters the room"
+    ],
+    "q_uid": "GX010029_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "Why was the man wearing a green t-shirt able to open the door?",
+    "options": [
+      "He pushed it with force",
+      "He showed the badge infront of the scanner",
+      "The door was not locked",
+      "Someone opened it for him"
+    ],
+    "q_uid": "GX010029_Clip_1.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the outfit color of the fifth person entering the room?",
+    "options": [
+      "Black",
+      "Brown",
+      "Green",
+      "Orange"
+    ],
+    "q_uid": "GX010029_Clip_1.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people entered the room wearing bags?",
+    "options": [
+      "One",
+      "Two",
+      "Three",
+      "Four"
+    ],
+    "q_uid": "GX010029_Clip_1.mp4"
+  },
+  {
+    "dimension": "Action Recognition",
+    "question": "What is the man in the black outfit doing in the scene?",
+    "options": [
+      "(A) Dragging a purple sack across the floor",
+      "(B) Pushing a black suitcase over the floor",
+      "(C) Pushing a black trolley over the floor",
+      "(D) None of the above"
+    ],
+    "q_uid": "GX010011_Clip_11.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What is the color of the partially visible bin beside the red bin that is visible after the white door of the room opens?",
+    "options": [
+      "White",
+      "Dark Blue",
+      "Light Blue",
+      "Green"
+    ],
+    "q_uid": "GX010011_Clip_11.mp4"
+  },
+  {
+    "dimension": "Attribute perception",
+    "question": "What color T-shirt is the person holding one box in his hand wearing?",
+    "options": [
+      "Black",
+      "Green",
+      "White",
+      "Red"
+    ],
+    "q_uid": "GX010069_Clip_4.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "In which direction are the people holding boxes turning to enter the room?",
+    "options": [
+      "Turning to their Left",
+      "Turning to their Right",
+      "Left of the video",
+      "Going straight"
+    ],
+    "q_uid": "GX010069_Clip_4.mp4"
+  },
+  {
+    "dimension": "Action reasoning",
+    "question": "How many people walked inside the room?",
+    "options": [
+      "1",
+      "2",
+      "3",
+      "4"
+    ],
+    "q_uid": "GX010069_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people left the room before the door was closed?",
+    "options": [
+      "3",
+      "5",
+      "10",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_10.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people went to left and right after the door opened?",
+    "options": [
+      "Left - 1 & Right -1",
+      "Right -1 & Left - 4",
+      "no one left the room after the door was opened",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_10.mp4"
+  },
+  {
+    "dimension": "Object recognition",
+    "question": "Which of the items did it not see in the video?",
+    "options": [
+      "table",
+      "chair",
+      "boxes",
+      "black stand"
+    ],
+    "q_uid": "GX010011_Clip_9.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many members entered the room after punching their cards?",
+    "options": [
+      "4",
+      "5",
+      "6",
+      "3"
+    ],
+    "q_uid": "GX010011_Clip_9.mp4"
+  },
+  {
+    "dimension": "Spatial perception",
+    "question": "Where was the box placed by the last person walking through the door?",
+    "options": [
+      "On the ground",
+      "On another box",
+      "On a white desk",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_4.mp4"
+  },
+  {
+    "dimension": "Counting problems",
+    "question": "How many people walk while holding boxes?",
+    "options": [
+      "Two",
+      "Three",
+      "Four",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_4.mp4"
+  }
+]

data/vqa/data_jsons/annotations/metrics_temporal_filtered_ss.json

@@ -0,0 +1,220 @@
+[
+  {
+    "dimension": "Temporal reasoning",
+    "question": "Which of the following is true according to the segment?",
+    "options": [
+      "everyone is holding something",
+      "some people may have empty hands",
+      "no one is carrying a laptop",
+      "only one person at a time can leave the room"
+    ],
+    "q_uid": "GX010071_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "In the scene, what caused the transparent gate to open?",
+    "options": [
+      "A man touching a metal part of the transparent gate",
+      "A man scanning a card beside the transparent gate",
+      "The door opens automatically as the person approaches",
+      "None of the above"
+    ],
+    "q_uid": "GX010071_Clip_6.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "In the scene, what is causing the transparent gates to open?",
+    "options": [
+      "People scanning cards beside the transparent doors",
+      "People clicking a button beside the transparent doors",
+      "It senses a person approaching and opens automatically",
+      "None of the above"
+    ],
+    "q_uid": "GX010032_Clip_2.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What is causing the transparent gates to open?",
+    "options": [
+      "People scanning cards beside the gates",
+      "People pushing a button beside the gates",
+      "When someone approaches, the gate automatically opens after detecting their presence",
+      "None of the above"
+    ],
+    "q_uid": "GX010014_Clip_1.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "Why was the person wearing a black T-shirt stopped by others near the door?",
+    "options": [
+      "was for higher level",
+      "had a no-black shirts policy",
+      "privacy purpose",
+      "both a and c"
+    ],
+    "q_uid": "GX010031_Clip_3.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "Why did the door on the right close?",
+    "options": [
+      "A person pulling the door",
+      "A person pushing the door",
+      "A person rotating the door handle",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_6.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "In the scene, what is causing the turnstiles to open?",
+    "options": [
+      "People pushing a button beside the turnstile",
+      "People scanning cards beside the turnstile",
+      "When someone approaches the turnstile, it detects their presence and opens automatically",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_2.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the door to close?",
+    "options": [
+      "The door being closed automatically",
+      "A person closing the door",
+      "A person scanning a card beside the door",
+      "None of the above"
+    ],
+    "q_uid": "GX010069_Clip_8.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What is the least likely reason the person stood alone, folding his hands, before others?",
+    "options": [
+      "had permission to exit early",
+      "finished the work early",
+      "skipped a meeting and reached early",
+      "a secret force pulled him ahead"
+    ],
+    "q_uid": "GX010070_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the door to close?",
+    "options": [
+      "closed automatically",
+      "a man pushing the door",
+      "a man pulling the door",
+      "none of the above"
+    ],
+    "q_uid": "GX010069_Clip_5.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the transparent gate to open and close?",
+    "options": [
+      "People open and close it manually",
+      "The gate has sensors",
+      "There is a switch near the gate",
+      "The gate opens and closes randomly"
+    ],
+    "q_uid": "GX010012_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the transparent doors to open in the video segment?",
+    "options": [
+      "A vehicle passing through",
+      "A man scanning a card beside the transparent doors",
+      "Objects passing through",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_1.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the door to open?",
+    "options": [
+      "A person swiping a card beside the door",
+      "A person pushing the door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_3.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the door to open?",
+    "options": [
+      "A person scanning a card beside the door",
+      "A person rotating the door handle and pushing the door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_7.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "When the first person walked up to the transparent door, why did it open?",
+    "options": [
+      "The person scanned a card beside the door",
+      "The door detects the person and automatically opens",
+      "The person clicked a button beside the door",
+      "None of the above"
+    ],
+    "q_uid": "GX010030_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "Why does the man wearing the black outfit near the door push a small card against the black object on the white wall beside the door?",
+    "options": [
+      "To change the color of the wall",
+      "To break the door open",
+      "To gain access to the room and unlock the door",
+      "None of the above"
+    ],
+    "q_uid": "GX010029_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "Did two people in the video tailgate?",
+    "options": [
+      "Yes",
+      "No"
+    ],
+    "q_uid": "GX010069_Clip_4.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the people who entered the room to close the door twice?",
+    "options": [
+      "maintain privacy",
+      "avoid distractions",
+      "focus on work",
+      "all of the above"
+    ],
+    "q_uid": "GX010011_Clip_9.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the third person not to punch his card while entering the room?",
+    "options": [
+      "forgot his card",
+      "had no card",
+      "Forgot to punch the card",
+      "all of the above"
+    ],
+    "q_uid": "GX010011_Clip_9.mp4"
+  },
+  {
+    "dimension": "Temporal reasoning",
+    "question": "What caused the door to open?",
+    "options": [
+      "A man swiping a card beside the door",
+      "A man rotating the door handle and pushing the door",
+      "Both a and b",
+      "None of the above"
+    ],
+    "q_uid": "GX010011_Clip_4.mp4"
+  }
+]

data/vqa/data_jsons/annotations/metrics_temporal_wo_ss.json

@@ -0,0 +1,24 @@
+[
+  {
+    "q_uid": "concat_wh_52_0_0.mp4",
+    "dimension": "Temporal reasoning",
+    "question": "What caused the box to fall?",
+    "options": [
+      "The black object under the box moves away",
+      "A person hits the box",
+      "An object hits the box",
+      "None of the above"
+    ]
+  },
+  {
+    "q_uid": "concat_wh_52_0_1.mp4",
+    "dimension": "Temporal reasoning",
+    "question": "What caused the box to fall?",
+    "options": [
+      "The box is not properly placed on the moving black object",
+      "A person hitting the box",
+      "An object hits the box",
+      "None of the above"
+    ]
+  }
+]