Skip to Main Content
Tracking human motion from monocular video sequences has attracted significantly increased interests in recent years. A key to accomplishing this task is to efficiently explore a high-dimensional state space. However, the traditional particle filter method and many of its variants have not been able to meet expectations as they lack a strategy to do efficiently sampling or stochastic search. We present a novel approach, namely differential evolution-Markov chain (DE-MC) particle filtering. By taking the advantage of the DE-MC algorithm's ability to approximate complicated distributions, substantial improvement can be made to the traditional structure of the particle filter. As a result, an efficient stochastic search can be performed to locate the modes of likelihoods. Furthermore, we apply the proposed algorithm to solve the 3D articulated model-based human motion tracking problem. A reliable image likelihood function is built for visual tracker design. Based on the proposed DE-MC particle filter and the image likelihood function, we perform a variety of monocular human motion tracking experiments. Experimental results, including the comparison with the performance of other particle filtering methods demonstrate the reliable tracking performance of the proposed approach.