Xiaopin Zhong

Tracking Multiple Visual Targets via Particle-Based Belief Propagation

Multiple-target tracking in video (MTTV) presents a technical challenge in video surveillance applications. In this paper, 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 the multitarget; 2) a binary random process for the existence of each individual target; and 3) a binary random process for the occlusion of each dual adjacent target. To make the inference tractable, we introduce two robust functions that eliminate the two binary processes. We then propose a novel belief propagation (BP) algorithm called particle-based BP and embed it into a Markov chain Monte Carlo approach to obtain the maximum a posteriori estimation in the DMN. With a stratified sampler, we incorporate the information obtained from a learned bottom-up detector (e.g., support-vector-machine-based classifier) and the motion model of the target into the message propagation. 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 the results suggest that our method is comparable to the state-of-art multitarget tracking methods in all the cases we tested.

Graphical Model based Cue Integration Strategy for Head Tracking

To achieve robust system, more and more vision researchers take into account fusing multiple visual cues. In this paper, we propose a novel strategy to integrate multiple naive cues for head tracking. Firstly, a cue dependency model is constructed via graphical model. Secondly, a new inference procedure based on non-parametric belief propagation is built for cue integration. The work presented is thus a general framework easy to extend for other computer vision research problems. Experimental results imply that the strategy we propose is effective, and it is robust without estimation of cue reliability.

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.

Pseudo measurement based multiple model approach for robust player tracking

This paper presents a robust player tracking method for sports video analysis. In order to track agile player stably and robustly, we employ multiple models method, with a mean shift procedure corresponding to each model for player localization. Furthermore, we define pseudo measurement via fusing the measurements obtained by mean shift procedure. And the fusing coefficients are built from two likelihood functions: one is image based likelihood; the other is motion based association probability. Experimental results show effectiveness of our method in the hard case of player tracking literature.

Sequential stratified sampling belief propagation for multiple targets tracking

In this paper, we model occlusion and appearance/disappearance in multi-target tracking in video by three coupled Markov random fields that model the following: a field for joint states 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 resulted dynamic Markov network. By using stratified sampler, we incorporate bottom-up information provided by a learned detector (e.g. SVM classifier) and belief information for the messages updating. Other low-level visual cues (e.g. color and shape) can be easily incorporated in our multi-target tracking model to obtain better tracking results. Experimental results suggest that our methods are comparable to the state-of-the-art multiple targets tracking methods in several test cases.