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

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<mark>Journal publication date</mark>02/2017
<mark>Journal</mark>Personal and Ubiquitous Computing
Issue number1
Volume21
Number of pages11
Pages (from-to)125-135
Publication StatusPublished
Early online date2/12/16
<mark>Original language</mark>English

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.