Self-occlusion handling for human body motion tracking from 3D ToF image sequence

Abstract

A 3D Time-of-flight (ToF) image is very useful to accurately track the human body motion due to its precision. However, the ToF image can not provide occluded 3D data because it also has a limitation of camera viewpoint. This paper proposes a self-occlusion handling scheme for human body motion tracking from 3D ToF image sequence. The proposed self-occlusion handling scheme consists of two steps: detect whether the body part is occluded or not and then estimate its motion from estimating the motion of non-occluded its adjacent body parts. Occlusion can be easily detected by using the eigenvalue analysis of 3D ToF data gathered from the joint point of each body part, and their motions can be estimated by calculating the rotation of the occluded body part. To apply it to the human body motion tracking, we use the Iterative closest point (ICP) algorithm and particle filter to track even the motion of fast moving body parts. Experimental results show that the human body motion tracking with the proposed self-occlusion handling scheme can correctly estimate even the motion of the self-occluded body part by comparing the estimated joint points with the manually marked joint points.