IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
Breckenridge, Colorado
January 05-January 07
ISBN: 0-7695-2271-8
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.
Citation:
Mun Wai Lee, Ramakant Nevatia, "Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach," wacv-motion, vol. 2, pp.168-175, IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2, 2005