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ACCV
2006
Springer

Tracking Targets Via Particle Based Belief Propagation

10 years 7 months ago
Tracking Targets Via Particle Based Belief Propagation
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.
Jianru Xue, Nanning Zheng, Xiaopin Zhong
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ACCV
Authors Jianru Xue, Nanning Zheng, Xiaopin Zhong
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