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--- |
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pretty_name: Tracks |
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license: cc-by-4.0 |
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tags: |
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- computer-vision |
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- human-motion |
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- robotics |
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- trajectory |
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- pose-estimation |
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- navigation |
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- retail |
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task_categories: |
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- image-feature-extraction |
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- keypoint-detection |
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- object-detection |
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- image-segmentation |
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- reinforcement-learning |
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size_categories: |
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- 1M<n<100M |
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--- |
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# Tracks Dataset |
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**Real Human Motion for Robotics Planning and Simulation** |
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**The ros2 docker container compiliation and visualization script instructions can be found here: https://huggingface.co/datasets/standard-cognition/Tracks/blob/main/keypoint-db/README.md** |
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--- |
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## Overview |
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<video src="https://huggingface.co/datasets/standard-cognition/Tracks/resolve/main/assets/visual.mp4" |
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autoplay |
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loop |
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muted |
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playsinline |
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style="width:100%;height:auto;border-radius:8px;"> |
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Your browser does not support the video tag. |
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</video> |
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The **Tracks Dataset** captures continuous, real-world human movement in retail environments, providing one of the largest and most structured pose-based trajectory corpora available for **robotics** and **embodied AI** research. |
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Each record represents **3D pose sequences** sampled at 10 Hz across normalized store coordinates, enabling research in motion planning, human-aware navigation, and humanoid gait learning derived directly from real behavior :contentReference[oaicite:0]{index=0}. |
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--- |
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## Key Specifications |
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| Field | Description | |
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|:------|:-------------| |
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| **Source** | Anonymized in-store multi-camera captures (10 retail sites) | |
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| **Scope** | ≈ 60,000 hours of human trajectory data (plus 1-hour evaluation subset) | |
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| **Format** | CSV schema, ROS 2–compatible via playback plug-in | |
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| **Sampling Frequency** | 10 Hz (10 FPS) | |
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| **Pose Structure** | 26 keypoints per person per frame (3D coordinates) | |
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| **Environment** | Real retail environments with normalized floor layouts | |
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| **Evaluation Subset** | One-hour segment including trajectories + store layout | |
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| **Key Metrics** | ≈ 2.3 M unique shoppers | |
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| **Anonymization** | Face and body suppression; coordinate-only representation | |
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| **Governance** | Managed under Standard AI’s data governance policies aligned with GDPR/CCPA and Responsible AI principles | |
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--- |
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## Integration & Applications |
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- Distributed in **CSV** with schema documentation and import notebooks. |
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- Ready for **ROS 2** integration for **path planning** and **human–robot interaction** simulation. |
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- Compatible with **Python**, **PyTorch**, and standard **reinforcement-learning** frameworks. |
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### Example Research Uses |
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- Motion prediction and trajectory planning |
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- Reinforcement learning for humanoid gait and control |
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- Human-aware navigation and avoidance behavior |
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- Simulation of human–robot interaction environments |
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--- |
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## Access |
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The **Tracks Dataset** is available now for evaluation and licensing. |
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- **Evaluation subset:** 1-hour sample under 30-day Evaluation Agreement (private Hugging Face repo). |
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- **Full dataset:** 60,000-hour commercial dataset available by request. |
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For inquiries or licensing: |
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✉️ [[email protected]](mailto:[email protected]) |
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--- |
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## Citation |
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```bibtex |
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@dataset{standardlabs_tracks_2025, |
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title = {Tracks Dataset: Real Human Motion for Robotics Planning and Simulation}, |
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author = {Standard Labs}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/datasets/standard-labs/tracks} |
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} |
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