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.
Tracking Targets Via Particle Based Belief Propagation
Computer Vision – ACCV 2006 ; 4 ; 348-358
Lecture Notes in Computer Science ; 3851 , 4
2006-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Markov Chain Monte Carlo , Gaussian Mixture Model , Hockey Player , Markov Network , Binary Process Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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