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We present a markerless human motion capture system that estimates the 3D positions of the body joints over time. The system uses a dynamic bayesian network and a factored particle filtering algorithm. In this paper we evaluate the impact of using different observation functions for the bayesian state estimation: chamfer distance, a pixel intersection and finally a pseudo-observation of the subject...
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