A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking

This paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. We perform tracking in different cues by interacting processes. Each process is represented by a Hidden Markov Model, and these parallel processes are arranged in a chain topology. The resulting Linked Hidden Markov Models naturally allow the use of particle filters and Belief Propagation in a unified framework. In particular, a target is tracked in each cue by a particle filter, and the particle filters in different cues interact via a message passing scheme. The general framework of our approach allows a customized combination of different cues in different situations, which is desirable from the implementation point of view. Our examples selectively integrate four visual cues including color, edges, motion and contours. We demonstrate empirically that the ordering of the cues is nearly inconsequential, and that our approach is superior to other approaches such as Independent Integration and Hierarchical Integration in terms of flexibility and robustness.