Adaptive particle filter with body part segmentation for full body tracking

Abstract

This paper presents a novel approach for marker-less 3D full body pose tracking using adaptive particle filter. Firstly, the search space decomposition strategy and body part segmentation method are used to reduce the calculation complexity due to the large degrees of freedom. Then an adaptive particle filter is adopted to track each body part. This new technique is a significant improvement over the standard particle filter with the advantage of adaptive particle number for each body part. Experimental results on tracking several challenging action sequences have shown that the proposed 3D full body tracker is able to effectively handle rapid no-linear movements, large changes of viewpoint, and different actors. The average errors of joint position are from 0.56 to 1.13 voxel in these action sequences.