Monocular Human Motion Tracking with the DE-MC Particle Filter
Ming Du, Ling Guan · 2006
A key to accomplish articulated human motion tracking and other high-dimensional visual tracking tasks is to have an efficient way to draw samples from the state space. The typical particle filter method and most of its variants do not perform well in achieving this goal. To solve the problem we present a novel algorithm, namely the differential evolution-Markov chain (DE-MC) particle filtering. It substantially improves the core of traditional particle filter, i.e. the sampling strategy. As a result, we can obtain reasonably distributed samples in an efficient way thus translating into reliable tracking performance. Experimental results demonstrate the power of the proposed approach