Probabilistic Multiple Cue Integration for Particle Filter Based Tracking

Robust visual tracking has become an important topic in the field of computer vision. The integration of cues such as color, edge strength and motion has proved to be a promising approach to robust visual tracking in situations where no single cue is suitable. In this paper, an algorithm is presented which integrates multiple cues in a probabilistic manner. Specifically the likelihood of each cue is calculated and weighted before Bayes’ rule is applied to obtain the resultant posterior. This posterior is generally not well represented analytically, and is therefore represented as a set of weighted particles, which is updated at each frame by a particle filter. This paper demonstrates how the combination of multiple cue integration and particle filtering results in a robust tracking method. We also demonstrate how each cue’s weight can be adapted on-line during the tracking procedure.