2026.3.1.21.34

GT/task_day_left_turn/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_day_occlusion/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_day_pedestrian_crossing/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_day_right_turn/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_day_right_turn_occlusion/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_day_right_turn_pedestrian/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_night_far_objects/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_night_occlusion/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_night_occlusion_far_objects/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_night_right_turn_pedestrian/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_night_right_turn_pedestrian_occlusion/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

GT/task_tunnel/gt/labels.txt

@@ -1,15 +0,0 @@
-person
-bus_small
-bus_big
-truck_small
-truck_big
-car
-bike
-motorbike
-ignore_region
-tractor
-trailor
-wheelchair
-heavy_equipment
-pm
-umbrella

Videos_/task_day_right_turn.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:024dc305add0da14d7d37c445c089f4f7ea003113c660acd4afca2ed1be18097
-size 17972349

Videos_/task_day_right_turn_occlusion.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:16414e766e0c3b721cbd573e4955e5a2fc9ca4b596d718dee963428cfb634822
-size 17795404

Videos_/task_day_right_turn_pedestrian.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:11949b92474ffd1b27b33617ca8f53a952c801cd00f4e0bec1fa9e12d5d5f1fe
-size 17085242

Videos_/task_night_far_objects.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c46a6698a0a86185317c236ac61aeb838a6223d98f4e66df0037576135470171
-size 29490160

Videos_/task_night_occlusion.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:627c0dc4ea2145538b19d1610c555eddc48740863c0155e998c3c1553ddebbba
-size 15835611

Videos_/task_night_right_turn_pedestrian.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:87ef2a06a722c2bebc0c5b7dd08b8072c9b22873f9e9d1dafd1836cb52a685f8
-size 16784463

Videos_/task_night_right_turn_pedestrian_occlusion.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ad233e78a5da58e971afc38a13066992fef721e3f8f2a305cbf6164bad38515a
-size 18553799

Videos_/task_tunnel.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:213b8f7c82c2e7c6d12bda5190a341950471a332dcd81315103c2ee9088cc2e7
-size 31518293

convert_to_coco.py

@@ -1,104 +0,0 @@
-import os
-import json
-import cv2
-import numpy as np
-
-# Paths
-FRAMES_DIR = "test_frame"
-GT_DIR = "GT"
-OUT_PATH = "annotations"
-os.makedirs(OUT_PATH, exist_ok=True)
-
-# Output COCO-style JSON
-out_file = os.path.join(OUT_PATH, "train.json")
-
-out = {
-    "images": [],
-    "annotations": [],
-    "videos": [],
-    "categories": [
-        {"id": 1, "name": "pedestrian"}  # You can expand with more classes if needed
-    ]
-}
-
-image_cnt = 0
-ann_cnt = 0
-video_cnt = 0
-tid_curr = 0
-tid_last = -1
-
-# Loop over sequences (one per video)
-for seq in sorted(os.listdir(FRAMES_DIR)):
-    seq_path = os.path.join(FRAMES_DIR, seq)
-    if not os.path.isdir(seq_path):
-        continue
-
-    video_cnt += 1
-    out["videos"].append({"id": video_cnt, "file_name": seq})
-
-    # Frames
-    images = sorted([f for f in os.listdir(seq_path) if f.endswith(".jpg")])
-    num_images = len(images)
-
-    for i, img_name in enumerate(images):
-        img_path = os.path.join(seq_path, img_name)
-        img = cv2.imread(img_path)
-        if img is None:
-            continue
-        height, width = img.shape[:2]
-
-        image_info = {
-            "file_name": f"{seq}/{img_name}",
-            "id": image_cnt + i + 1,
-            "frame_id": i + 1,
-            "prev_image_id": image_cnt + i if i > 0 else -1,
-            "next_image_id": image_cnt + i + 2 if i < num_images - 1 else -1,
-            "video_id": video_cnt,
-            "height": height,
-            "width": width
-        }
-        out["images"].append(image_info)
-
-    # Load GT file
-    gt_path = os.path.join(GT_DIR, seq, "gt", "gt.txt")
-    if not os.path.exists(gt_path):
-        print(f" No GT found for {seq}, skipping annotations.")
-        image_cnt += num_images
-        continue
-
-    anns = np.loadtxt(gt_path, dtype=np.float32, delimiter=",")
-
-    for i in range(anns.shape[0]):
-        frame_id = int(anns[i][0])
-        track_id = int(anns[i][1])
-        x, y, w, h = anns[i][2:6]
-        conf = anns[i][6]
-        class_id = int(anns[i][7])
-        visibility = anns[i][8] 
-
-        ann_cnt += 1
-        if track_id != tid_last:
-            tid_curr += 1
-            tid_last = track_id
-
-        ann = {
-            "id": ann_cnt,
-            "category_id": class_id,  
-            "image_id": image_cnt + frame_id,
-            "track_id": tid_curr,
-            "bbox": [float(x), float(y), float(w), float(h)],
-            "conf": float(conf),
-            "iscrowd": 0,
-            "area": float(w * h),
-        }
-        out["annotations"].append(ann)
-
-    image_cnt += num_images
-
-print(f" Loaded {len(out['images'])} images and {len(out['annotations'])} annotations.")
-
-# Save JSON
-with open(out_file, "w") as f:
-    json.dump(out, f)
-
-print(f" Saved COCO-style annotations to {out_file}")

extract_frames.py

@@ -1,60 +0,0 @@
-import os
-import cv2
-import numpy as np
-
-# Paths
-videos_dir = "test_vid"
-frames_dir = "test_frame"
-
-# Define how many frames to extract for each video (should be same in GT file last row)
-frames_to_extract = {
-    "task_day_left_turn-2024_07_31_02_12_05-mot 1.1.mp4": 1965,
-    # add more here...
-}
-
-os.makedirs(frames_dir, exist_ok=True)
-
-for video_file in os.listdir(videos_dir):
-    if video_file.lower().endswith(".mp4"):
-        video_path = os.path.join(videos_dir, video_file)
-
-        # Subdirectory name (remove .mp4)
-        subdir_name = os.path.splitext(video_file)[0]
-        subdir_path = os.path.join(frames_dir, subdir_name)
-        os.makedirs(subdir_path, exist_ok=True)
-
-        cap = cv2.VideoCapture(video_path)
-        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        desired_frames = frames_to_extract.get(video_file, total_frames)
-
-        saved = 0
-        frames = []
-
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frames.append(frame)
-
-        cap.release()
-
-
-        if desired_frames > len(frames):
-            last_frame = frames[-1]
-            while len(frames) < desired_frames:
-                frames.append(last_frame)
-
-
-        if desired_frames < len(frames):
-            indices = np.linspace(0, len(frames) - 1, desired_frames, dtype=int)
-            frames = [frames[i] for i in indices]
-
-
-        for idx, frame in enumerate(frames):
-            frame_filename = os.path.join(subdir_path, f"frame_{idx:05d}.jpg")
-            cv2.imwrite(frame_filename, frame)
-            saved += 1
-
-        print(f"Extracted {saved} frames from {video_file} into {subdir_name}/")
-
-print(" Done extracting frames for all videos.")

train/task_day_left_turn.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18c060aec288d8790ecfbd89bb7c1068f16427672da6f9ea6e0c4138dcd38560
+size 1608160074

train/task_day_occlusion.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33ab4cd7986c360968836b8a99a9603c8acee433abc186c155064eb7b8530dad
+size 1571091360

train/task_day_pedestrian_crossing.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e5b9d960b8966ffd2e65a880983cdd249a37522184f081477da20a0276c324a
+size 1379301049

train/task_day_right_turn.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d06ac3702da134f9f4bf6d1d306b1eb530ab09a3be256e6d24784ec440b5afe
+size 1299660850

train/task_day_right_turn_occlusion.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63d16f864e72e62e52773cb2f303c55a88fed831099055739f0f5d07b982ae6f
+size 1639623106

train/task_day_right_turn_pedestrian.zip

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+version https://git-lfs.github.com/spec/v1
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+size 1554372760

Videos_/night_occlusion_far_objects.mp4 → train/task_night_far_objects.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9eff2bb95e195a14f7a2b9cf83e7a10e85a7cef22a36a87719fd6dd0c9157cb0
-size 29183584
+oid sha256:7229a4d32d05d0c9079075fe0c1e0fe8e189a6a41ec291c5231ddeff6bf5b5d3
+size 688640068

Videos_/task_day_left_turn.mp4 → train/task_night_occlusion.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b4fde5aefb66ffd282e35ef0217a52c8d59acf373314d1aac9d4468fcc65b823
-size 19418590
+oid sha256:e94fd05089bc276768224d2571b8aab84cdf3506e0a5e2c15043e904c3aca192
+size 985174903

Videos_/task_day_occlusion.mp4 → train/task_night_occlusion_far_objects.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:1cd4ecd2e6dd52986812930666a02adad1541a9c3484094a3552509de5a2a026
+size 664973449

train/task_night_right_turn_pedestrian.zip

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+size 1403856209

train/task_night_right_turn_pedestrian_occlusion.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b84978f21611a54b96548991effea467ae9064ff967939c9b0640197fb65018e
+size 1412154558

Videos_/task_day_pedestrian_crossing.mp4 → train/task_tunnel.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:365ffbaf54424f7e303a0fc71ba3dfd97dcd24dadfac08d64d08e41b61ff79ca
-size 16706342
+oid sha256:5daa3c2ad1cff3a7681b7ce28d6a4c176aaaa4e9282bd6fbb4de5401738e6a5a
+size 549196007