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2005
IEEE

Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach

11 years 11 months ago
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
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.
Mun Wai Lee, Ramakant Nevatia
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where WACV
Authors Mun Wai Lee, Ramakant Nevatia
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