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OpenPTrack is an open source project launched in 2013 to create a scalable, multi-camera solution for person tracking, to support applications in education, art and culture.
This wiki refers to the v2 "Gnocchi release".
Our objective is to enable “creative coders” to create body-based interfaces for large groups of people—for classrooms, art projects and beyond.
Based on the widely used, open source Robot Operating System (ROS), OpenPTrack provides:
- user-friendly camera network calibration;
- person detection from RGB/infrared/depth images;
- efficient multi-person tracking;
- object tracking from RGB and depth images;
- reliable multiple-object tracking; and
- multi-camera and multi-person pose annotation.
- UDP and NDN streaming of tracking data in JSON format.
With the advent of commercially available consumer depth sensors, and continued efforts in computer vision research to improve multi-modal image and point cloud processing, robust person tracking with the stability and responsiveness necessary to drive interactive applications is now possible at low cost. But the results of the research are not easy to use for application developers.
We believe that a disruptive project is needed for artists, creators and educators to work with robust real-time person tracking in real-world projects. OpenPTrack aims to support those in the arts and cultural and education sectors who wish to experiment with real-time person & object tracking along with pose annotation, as an input for their applications. The project contains numerous state-of-the-art algorithms for RGB and/or depth tracking, and has been created on top of a modular node-based architecture, to support the addition and removal of different sensor streams online.
OpenPTrack is led by UCLA REMAP and Open Perception. Key collaborators include the University of Padova, Electroland, and Indiana University Bloomington. Code is available under a BSD license. Portions of the work are supported by the National Science Foundation (IIS-1323767).
Follow us on Twitter: @openptrack.
For further documentation, please see the navigation bar to the right.
OpenPTrack is an open source project. If you discover optimizations, or if there is a feature you'd like to contribute, please make a branch for your feature and submit a pull request.
- System Requirements
- Supported Hardware
- Initial Network Configuration
- Example Hardware List for UCLA Setup
- Making the Checkerboard
- Time Synchronization
- Pre-Tracking Configuration
- Camera Network Configuration
- Single Camera
- Setting Parameters
- Multi-Sensor Person Tracking
- HOG vs YOLO Detectors
- World Coordinate Settings
- Single Camera
- Pose Initialization
- Multi Sensor Pose Annotation
- Pose Best Practices
- Setting Parameters
- Single Camera
- Setting Parameters
- Multi Sensor Object Tracking
- YOLO Custom Training & Testing
- Yolo Trainer
- Single Camera
- Setting Parameters
- Multi Sensor Face Detection and Recognition
- Face Detection and Recognition Data Format
How to receive tracking data in: