Multiple Hypothesis Tracking for Automatic Optical Motion Capture
Lecture notes in computer sciencePublished 1 January 2002
Maurice Ringer, Joan Lasenby
Citations21
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A technique for performing the tracking stage of optical motion capture is presented which retains, at each time frame, multiple marker association hypotheses and estimates of the subject's position using a Bayesian approach.
Abstract
We present a technique for performing the tracking stage of optical motion capture which retains, at each time frame, multiple marker association hypotheses and estimates of the subject's position. Central to this technique are the equations for calculating the likelihood of a sequence of association hypotheses, which we develop using a Bayesian approach. The system is able to perform motion capture using fewer cameras and a lower frame rate than has been used previously, and does not require the assistance of a human operator. We conclude by demonstrating the tracker on real data and provide an example in which our technique is able to correctly determine all marker associations and standard tracking techniques fail.
Keywords
Computer Science
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