initial datacard w/ visual

README.md

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

assets/visual.mp4

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