Multitarget Tracking in Video-Part I

Abstract

AbstractMultitarget tracking in video (MTTV) presents a technical challenge in video surveillance applications. In this chapter, we formulate the MTTV problem using dynamic Markov network (DMN) techniques. Our model consists of three coupled Markov random fields: (1) a field for the joint state of multiple targets; (2) a binary random process for flagging the existence of each individual target; and (3) a binary random process for flagging occlusion in each dual adjacent targets. To make inference tractable, we introduce two robust functions that eliminate the two binary processes. We then propose a novel belief propagation algorithm, called Particle-based belief propagation, and embed it into a Markov chain Monte Carlo approach to obtain the maximum a posteriori (MAP) estimation in the DMN. With a stratified sampler, we incorporate the information obtained from a learned bottom-up detector (e.g., support vector machine (SVM) based classifier) and the motion model of the target into a message propagation framework. Other low-level visual cues, such as motion and shape, can be easily incorporated into our framework to obtain better tracking results. We have performed extensive experimental verification, and results suggest that our method is comparable to the state-of-art multitarget tracking methods in all cases we tested.KeywordsMarkov Chain Monte CarloMonte CarloJoint StateData AssociationHockey PlayerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.