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Monocular Human Motion Tracking by Using DE-MC Particle Filter

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3 Author(s)
Ming Du ; Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA ; Xiaoming Nan ; Ling Guan

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.

Published in:

Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 10 )