Bayesian 3D Human Body Pose Tracking from Depth Image Sequences

Abstract

This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from depth image sequence is challenging due to the need to resolve depth ambiguity caused by self-occlusions and difficulty to recover from tracking failure. Human body poses could be estimated with a high accuracy based on local optimization using dense correspondences between 3D depth data and the vertices in an articulated human model. However, it cannot recover from tracking failure. This paper presents a method to reconstruct human pose by detecting and tracking human body anatomical landmarks (key-points) from depth images. The proposed method is robust and recovers from tracking failure when a body part is re-detected. However, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian method for integrating pose estimation results obtained by methods based on key-points and local optimization. Experimental results are shown and performance comparison is presented to demonstrate the effectiveness of the proposed method.