| --- |
| annotations_creators: [] |
| language: en |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - object-detection |
| task_ids: [] |
| pretty_name: RetailAction |
| tags: |
| - fiftyone |
| - group |
| - object-detection |
| dataset_summary: ' |
| |
| |
| |
| |
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 21000 samples. |
| |
| |
| ## Installation |
| |
| |
| If you haven''t already, install FiftyOne: |
| |
| |
| ```bash |
| |
| pip install -U fiftyone |
| |
| ``` |
| |
| |
| ## Usage |
| |
| |
| ```python |
| |
| import fiftyone as fo |
| |
| from fiftyone.utils.huggingface import load_from_hub |
| |
| |
| # Load the dataset |
| |
| # Note: other available arguments include ''max_samples'', etc |
| |
| dataset = load_from_hub("Voxel51/RetailAction") |
| |
| |
| # Launch the App |
| |
| session = fo.launch_app(dataset) |
| |
| ``` |
| |
| ' |
| --- |
| |
| # Dataset Card for RetailAction |
|
|
|  |
|
|
|
|
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 21000 samples. |
|
|
| ## Installation |
|
|
| If you haven't already, install FiftyOne: |
|
|
| ```bash |
| pip install -U fiftyone |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import fiftyone as fo |
| from fiftyone.utils.huggingface import load_from_hub |
| |
| # Load the dataset |
| # Note: other available arguments include 'max_samples', etc |
| dataset = load_from_hub("Voxel51/RetailAction") |
| |
| # Launch the App |
| session = fo.launch_app(dataset) |
| ``` |
| --- |
|
|
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| RetailAction is designed for spatio-temporal localization of **customer–product interactions** (`take`, `put`, `touch`) across synchronized multi-view ceiling-mounted cameras in real stores. |
|
|
| - **Curated by:** Standard AI — Davide Mazzini, Alberto Raimondi, Bruno Abbate, Daniel Fischetti, David M. Woollard |
| - **License:** Standard AI proprietary license (see [LICENSE](./LICENSE)) |
| - **Paper:** *RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail* — ICCV 2025 Retail Vision Workshop |
|
|
| ### Dataset Sources |
|
|
| - **Repository:** [standard-cognition/RetailAction on Hugging Face](https://huggingface.co/datasets/standard-cognition/RetailAction) |
| - **Paper:** [ICCV 2025 RetailAction paper](./Mazzini_RetailAction_Dataset_for_Multi-View_Spatio-Temporal_Localization_of_Human-Object_Interactions_in_ICCVW_2025_paper.pdf) |
|
|
| --- |
|
|
|
|
| ## FiftyOne Dataset Structure |
|
|
| The dataset is a **grouped video dataset** (`media_type = "group"`). Each RetailAction sample folder maps to one **FiftyOne Group** with two **video slices**: `rank0` (default) and `rank1`. Each slice is a `fo.Sample` pointing to the respective `.mp4` file, with all annotations stored per slice using the camera-specific coordinates. |
|
|
| ### Top-level dataset properties |
|
|
| ```python |
| dataset.media_type # "group" |
| dataset.group_field # "group" |
| dataset.group_slices # ["rank0", "rank1"] |
| dataset.group_media_types # {"rank0": "video", "rank1": "video"} |
| dataset.default_group_slice # "rank0" |
| dataset.skeletons # {"pose_keypoints": <KeypointSkeleton>} |
| ``` |
|
|
| ### Sample-level fields |
|
|
| These fields are present on every `fo.Sample` in both `rank0` and `rank1` slices. |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `sample_id` | `str` | Zero-padded folder name, e.g. `"000000"` | |
| | `split` | `str` | `"train"`, `"validation"`, or `"test"` | |
| | `tags` | `list[str]` | Also contains the split name for tag-based filtering | |
| | `segment_duration` | `float` | Original video segment duration in seconds (0.9–37s) | |
| | `has_frame_timestamps` | `bool` | `False` for ~12% of samples where frame timestamps were not recorded | |
| | `actions` | `fo.TemporalDetections` | One `fo.TemporalDetection` per annotated action | |
| | `interaction_points` | `fo.Detections` | One `fo.Detection` per annotated interaction point | |
|
|
| #### `actions` — `fo.TemporalDetections` |
|
|
| Each `fo.TemporalDetection` in `sample.actions.detections` represents one human–object interaction: |
|
|
| | Attribute | Type | Description | |
| |---|---|---| |
| | `label` | `str` | Action class: `"take"`, `"put"`, or `"touch"` | |
| | `support` | `[int, int]` | `[first_frame, last_frame]` (1-based, inclusive) derived from normalized `[start, end]` × total frames | |
|
|
| #### `interaction_points` — `fo.Detections` |
| |
| Each `fo.Detection` in `sample.interaction_points.detections` marks where the hand contacts the shelf item. Detections are parallel in order to `sample.actions.detections` (index `i` in both refers to the same action). |
|
|
| | Attribute | Type | Description | |
| |---|---|---| |
| | `label` | `str` | Action class: `"take"`, `"put"`, or `"touch"` | |
| | `bounding_box` | `[x, y, w, h]` | Small 4%×4% box centered on the interaction point (normalized, for App visibility) | |
| | `interaction_x` | `float` | Raw normalized x-coordinate of the interaction point (for metric computation) | |
| | `interaction_y` | `float` | Raw normalized y-coordinate of the interaction point (for metric computation) | |
|
|
| > **Note:** `interaction_x` / `interaction_y` preserve the exact annotated point coordinates for use in the paper's `m_px_factor`-based spatial distance metric. The bounding box is a visualization convenience only. |
|
|
| ### Frame-level fields |
|
|
| These fields are stored in `sample.frames[i]` (1-indexed) and are populated for each of the up to 32 selected video frames. Frames with no detected pose have `pose_keypoints = None`. |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `pose_keypoints` | `fo.Keypoints` | Body pose for the subject of interest | |
| | `face_position` | `fo.Keypoint` | Head center point of the subject of interest | |
| | `sampling_score` | `float` | Motion-aware frame importance score (higher = more hand movement) | |
|
|
| #### `pose_keypoints` — `fo.Keypoints` |
| |
| Each frame's `fo.Keypoints` contains one `fo.Keypoint` with label `"person"` representing the full-body pose of the interaction subject. |
| |
| - **Points:** Fixed-length list of 24 `[x, y]` coordinates in normalized frame space, one per joint in `JOINT_ORDER`. Missing joints (not detected by the pose model for that frame/view) are `[nan, nan]`. |
| - **Confidence:** Parallel list of 24 raw heatmap activation scores from the PersonLab model. Missing joints have `nan`. Scores are **not probabilities** — they are uncalibrated logit-like values typically in `[0.06, 1.41]`; values >1.0 are possible. |
|
|
| The canonical 24-joint ordering (`JOINT_ORDER`): |
|
|
| ``` |
| 0: top_of_head 1: nose 2: neck |
| 3: left_ear 4: right_ear 5: left_eye 6: right_eye |
| 7: left_shoulder 8: right_shoulder |
| 9: left_elbow 10: right_elbow |
| 11: left_wrist 12: right_wrist |
| 13: left_hand 14: right_hand |
| 15: middle_of_waist |
| 16: left_hip 17: right_hip |
| 18: left_knee 19: right_knee |
| 20: left_ankle 21: right_ankle |
| 22: left_foot 23: right_foot |
| ``` |
|
|
| The dataset's `skeletons["pose_keypoints"]` stores the `fo.KeypointSkeleton` with this ordering and 25 bone connectivity edges, enabling automatic skeleton rendering in the FiftyOne App. |
|
|
| **Important notes on pose data:** |
| - Joint sets vary per frame and per camera view — one view may detect the left arm while the other detects the lower body. |
| - Coordinates are quantized to a **1/64 grid** (PersonLab's heatmap resolution), giving a step size of 0.015625. |
| - ~9% of samples have at least one null pose entry due to the tracker losing the subject mid-segment. |
| - Only the **subject of interest** (the person performing the labeled interaction) has pose data. Other people in frame have no annotations. |
|
|
| #### `face_position` — `fo.Keypoint` |
| |
| Single-point keypoint with label `"face"` marking the detected head center of the subject of interest. |
| |
| - **Points:** `[[x, y]]` — normalized continuous float coordinates from the face detector (not quantized, unlike pose). |
| - Present for all frames where the subject's face was detected. |
| |
| #### `sampling_score` — `float` |
|
|
| Motion-aware importance score computed from the velocity and acceleration of the subject's hands. Higher scores indicate frames with significant hand movement. Used during dataset construction to select the most informative ≤32 frames from longer original segments. |
|
|
| - For ~12% of samples (`has_frame_timestamps = False`), scores are matched positionally (score `i` → frame `i`) rather than by timestamp. |
| - For the remainder, scores are matched to the nearest timestamp from the original dense score timeline. |
|
|
| ### Querying examples |
|
|
| ```python |
| import fiftyone as fo |
| |
| ds = fo.load_dataset("RetailAction") |
| |
| # Filter to samples with at least one action |
| ds_with_actions = ds.filter_labels("actions", fo.ViewField("support").length() > 0) |
| |
| # Get only test samples |
| test_view = ds.match_tags("test") |
| |
| # Get multi-action samples (2+ actions in one segment) |
| multi_action = ds.select_group_slices("rank0").filter_labels( |
| "actions", |
| fo.ViewField("detections").length() >= 2, |
| only_matches=False |
| ).match(fo.ViewField("actions.detections").length() >= 2) |
| |
| # Filter to frames where pose was detected |
| frames_with_pose = ds.match_frames(fo.ViewField("pose_keypoints") != None) |
| |
| # Switch to the second camera view |
| ds.group_slice = "rank1" |
| ``` |
|
|
| --- |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| RetailAction was created to fill a gap in existing action recognition datasets: no large-scale dataset provided **spatio-temporal localization** of interactions in **real retail stores** from **multiple synchronized camera views**. Prior retail datasets (MERL Shopping, RetailVision) were small, lab-based, or single-view. General-purpose datasets (Kinetics, AVA) lacked retail context and provided bounding boxes around people rather than precise interaction points. |
|
|
| ### Data Collection and Processing |
|
|
| Data was collected over multiple years from 10 operational US convenience stores. An automated pipeline handled the full flow from raw continuous camera streams to annotated clips: |
|
|
| 1. **360-degree cameras** (2880×2880, 30 FPS) mounted at ~2.5m ceiling height provided continuous multi-TB/day streams. |
| 2. A **custom PersonLab model** fine-tuned on 360-degree top-view footage estimated 2D poses per person per frame. |
| 3. **Multi-view 3D pose reconstruction** triangulated per-camera tracklets into unified 3D tracks. |
| 4. A **kinematic GCN** (based on ST-GCN) operating on 3D poses and shelf geometry detected candidate interaction intervals, filtering out walking and browsing. |
| 5. A **camera scoring algorithm** selected the two best views per interaction based on occlusion, body visibility, and hand joint visibility. |
| 6. **Motion-aware frame subsampling** down-sampled each clip to ≤32 frames by prioritizing frames with high hand velocity/acceleration. |
| 7. **Anonymization** applied facial blurring and timestamp scrubbing (all timestamps are relative to `1970-01-01T00:00:00`). |
|
|
| ### Annotations |
|
|
| Annotations were produced through a two-step human annotation process: |
|
|
| **Step 1 — Binary classification + quality labels:** Annotators labeled each segment as interaction/non-interaction and flagged quality issues (bad camera selection, low resolution, too few frames, pose errors). A model-in-the-loop strategy was used: after an initial labeling pass, a model was trained on half the data and the 10% most-disagreed samples were re-reviewed. This cycle repeated three times. |
|
|
| **Step 2 — Spatio-temporal fine-grained labels:** Annotators marked the precise temporal boundaries of each interaction and spatially localized the exact pixel where the hand contacts the shelf item, for both camera views. In multi-person scenes, a red dot overlay identified the subject of interest to avoid ambiguity. |
|
|
| **Action categories:** |
| - `take` — subject picks up an item from a shelf, fridge, or counter |
| - `put` — subject places an item back onto a shelf, fridge, or counter |
| - `touch` — hand contact without taking or placing |
|
|
| Labels apply only to interactions with retail shelves — not to shopping baskets, checkout interactions, or other in-store objects. |
|
|
| **Post-annotation curation** removed single-view segments, low-quality samples, outlier-duration segments, and excess no-interaction segments (capped at 10% of total). |
|
|
| ### Personal and Sensitive Information |
|
|
| All shoppers consented to recording via terms of service with the collecting organization. Videos have been anonymized: |
| - Faces are blurred using automated facial detection |
| - All timestamps are replaced with epoch-relative offsets (starting at `1970-01-01T00:00:00`) |
| - Store names and identifiers are removed or blurred |
| - Shopper identity labels are withheld — splits are partitioned by shopper but identifiers are not released |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| **Class imbalance:** 97.2% of labeled actions are `take`. The `put` and `touch` classes are heavily underrepresented, reflecting real customer behavior rather than a collection artifact. |
|
|
| **Store distribution skew:** Store 1 accounts for 36.2% of samples; stores 5–10 together account for <10%. Models trained on this dataset may generalize poorly to stores with unusual layouts or lighting. |
|
|
| **Top-down perspective:** All footage is from ceiling-mounted cameras. Models trained here are not expected to generalize to handheld, egocentric, or eye-level viewpoints. |
|
|
| **Partial pose observations:** Due to occlusion and the 360-degree fisheye distortion, ~20% of joint detections have low confidence (<0.5), and the detected joint set varies considerably per frame. |
|
|
| **Non-uniform frame rate:** Clips contain ≤32 frames but span segments of 0.9–37 seconds. The effective frame rate is non-uniform and lower than the original 30 FPS. Temporal models must account for variable time gaps between frames. |
|
|
| **Null frame timestamps:** ~12% of samples lack `frame_timestamps`, preventing precise temporal alignment of pose and face data to wall-clock time. |
|
|
| ### Recommendations |
|
|
| - Apply a confidence threshold (e.g., >0.5) to `pose_keypoints.confidence` values before using joints for bone-length normalization or feature extraction. |
| - Use `has_frame_timestamps` to identify samples where frame-level temporal alignment is unavailable. |
| - For the spatial localization metric, use `interaction_x` / `interaction_y` attributes (not the bounding box center) and apply the per-video `m_px_factor` computed from bone lengths as described in the paper. |
| - When evaluating across action classes, report per-class metrics given the severe `take`/`put`/`touch` imbalance. |
|
|
| --- |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @inproceedings{mazzini2025retailaction, |
| title={RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail}, |
| author={Mazzini, Davide and Raimondi, Alberto and Abbate, Bruno and Fischetti, Daniel and Woollard, David M.}, |
| booktitle={ICCV Retail Vision Workshop}, |
| year={2025} |
| } |
| ``` |
|
|
| **APA:** |
|
|
| Mazzini, D., Raimondi, A., Abbate, B., Fischetti, D., & Woollard, D. M. (2025). RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail. *ICCV Retail Vision Workshop*. |
|
|
