A Sampling Algorithm for Occlusion Robust Multi Target Detection

Authors:
Oswald Lanz;Stefano Messelodi
Affiliations:
-;-
Venue:
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Year:
2009

Citing 6
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Abstract

Bayesian methods for visual tracking, with the particle filter as its most prominent instance, have proven to workeffectively 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.