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

Sample data can be found here: download from: https://drive.google.com/file/d/1sf924lEBK2VLuBmHUzGXUakn6blwY9r2/view?usp=sharing

The layout can be downloaded from: https://drive.google.com/file/d/1--ahjbkuLuZfeaxluWScoGz78k58O9Mt/view?usp=sharing

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Overview

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}.

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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

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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

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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

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Citation

@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}
}