Articulated human body pose tracking by suppression based immune particle filter

Abstract

Particle filter is a popular stochastic tracker for object tracking. In articulated human body pose tracking, lots of work focuses on increasing sampling efficiency by incorporating optimization algorithm into particle filter. In this study, we propose a modified optimization based particle filter algorithm for pose tracking. The new algorithm can maintain the diversity of particle set by using a suppression scheme. Experimental results show that the proposed method can cope with multi-modality and can obtain more accurate estimation than other optimization based particle filter methods.