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.

• Type: Conference
• Year: 2005
• Where: WACV
• Authors: Mun Wai Lee, Ramakant Nevatia
• Keywords: Automatic Pose Initialization, Human Body Pose, Markov Chain Monte Carlo, WACV 2005