Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking

Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions place more challenge on visual tracking. To overcome these difficulties, we formulate multiple targets tracking problem in a dynamic Markov network which consists of three coupled Markov random fields that model the following: a field for joint state of multi-target, one binary process for existence of individual target, and another binary process for occlusion of dual adjacent targets. By introducing two robust functions, we eliminate the two binary processes, and then apply a novel version of belief propagation called sequential stratified sampling belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the dynamic Markov network. By using stratified sampler, we incorporate bottom-up information provided by a learned detector (e.g. SVM classifie...

• Type: Conference
• Year: 2005
• Where: ICIC
• Authors: Jianru Xue, Nanning Zheng, Xiaopin Zhong
• Keywords: Dynamic Markov Network, ICIC 2005, Multiple Targets, Targets Tracking Problem