Abstract
Bayesian methods for visual tracking, with the particle filter as its most prominent instance, have proven to work effectively in the presence of clutter, occlusions, and dynamic background. When applied to track a variable number of targets, however, they become inefficient due to the absence of strong priors. In this paper we present an efficient sampling algorithm for target detection build upon an informed prior that is derived as the inverse of an occlusion robust image likelihood. It has the advantage of being fully integrated in the Bayesian tracking framework, and reactive as it uses sparse features not explained by tracked objects. © 2009 IEEE.