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Out of sight: a toolkit for tracking occluded human joint positions

Research output: Contribution to journalArticlepeer-review

Author(s)

Chi-Jui Wu, Aaron John Quigley, David Cameron Christopher Harris-Birtill

School/Research organisations

Abstract

Real-time identification and tracking of the joint positions of people can be achieved with off-the-shelf sensing technologies such as the Microsoft Kinect, or other camera-based systems with computer vision. However, tracking is constrained by the system’s field of view of people. When a person is occluded from the camera view, their position can no longer be followed. Out of Sight addresses the occlusion problem in depth-sensing tracking systems. Our new tracking infrastructure provides human skeleton joint positions during occlusion, by combining the field of view of multiple Kinects using geometric calibration and affine transformation. We verified the technique’s accuracy through a system evaluation consisting of 20 participants in stationary position and in motion, with two Kinects positioned parallel, 45°, and 90° apart. Results show that our skeleton matching is accurate to within 16.1 cm (s.d. = 5.8 cm), which is within a person’s personal space. In a realistic scenario study, groups of two people quickly occlude each other, and occlusion is resolved for 85% of the participants. A RESTful API was developed to allow distributed access of occlusion-free skeleton joint positions. As a further contribution, we provide the system as open source.
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Details

Original languageEnglish
Pages (from-to)125-135
Number of pages11
JournalPersonal and Ubiquitous Computing
Volume21
Issue number1
Early online date2 Dec 2016
DOIs
Publication statusPublished - Feb 2017

    Research areas

  • Kinect, Occlusion, Toolkit

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