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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.

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;
• 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 tracking 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] (http://www.electroland.net/), 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.

Guides

See documentation on our github wiki.