title: Monocular human motion tracking by using DE-MC particle filter.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. mingdu@umd.edu
authors.forename: Ming
authors.initials: M
authors.lastname: Du
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Nan
authors.forename: Ling
authors.initials: L
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D009068:Movement / Q000502:physiology*; D014741:Video Recording / Q000379:methods*