---
pretty_name: Tracks
license: cc-by-4.0
tags:
  - computer-vision
  - human-motion
  - robotics
  - trajectory
  - pose-estimation
  - navigation
  - retail
task_categories:
  - image-feature-extraction
  - keypoint-detection
  - object-detection
  - image-segmentation
  - reinforcement-learning
size_categories:
  - 1M<n<100M
---

# Tracks Dataset  
**Real Human Motion for Robotics Planning and Simulation**

**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**

---

## Overview

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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.  
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 |

---

## 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.

### 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

---

## 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:
✉️ [labs@standard.ai](mailto:labs@standard.ai)  

---

## 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}
}