This paper addresses the problem of tracking human body pose in monocular video including automatic pose initialization and re-initialization after tracking failures caused by partial occlusion or unreliable observations. We proposed a method based on data-driven Markov chain Monte Carlo (DD-MCMC) that uses bottom-up techniques to generate state proposals for pose estimation and initialization. This method allows us to exploit different image cues and consolidate the inferences using a representation known as the proposal maps. We present experimental results with an indoor video sequence.
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
01.01.2005
380216 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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