Abstract

This paper presents a novel probabilistic approach to inte- grating 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 topol- ogy. The resulting Linked Hidden Markov Models naturally allow the use of particle filters and Belief Propagation in a unified framework. In par- ticular, 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 in- cluding color, edges, motion and contours. We demonstrate empirically that the ordering of the cues is nearly inconsequential, and that our ap- proach is superior to other approaches such as Independent Integration and Hierarchical Integration in terms of flexibility and robustness.