Tracking targets via particle based belief propagation

Authors:
Jianru Xue;Nanning Zheng;Xiaopin Zhong
Affiliations:
Institute of Artificial Intelligence and Robotics, XI’AN Jiaotong University, Xi’an, China;Institute of Artificial Intelligence and Robotics, XI’AN Jiaotong University, Xi’an, China;Institute of Artificial Intelligence and Robotics, XI’AN Jiaotong University, Xi’an, China
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Year:
2006

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Abstract

We first formulate multiple targets tracking problem in a dynamic Markov network(DMN)which is derived from a MRFs for joint target state and a binary process for occlusion of dual adjacent targets. We then propose to embed a novel Particle based Belief Propagation algorithm into Markov Chain Monte Carlo approach (MCMC) to obtain the maximum a posteriori (MAP) estimation in the DMN. In the message propagation,a stratified sampler incorporates information both from a learned bottom-up detector (e.g. SVM classifier) and a top-down dynamic behavior model. Experimental results show that the proposed method is able to track varying number of targets and handle their interactions.