Dynamic integration of generalized cues for person tracking

Abstract

We present an approach for the dynamic combination of multiple cues in a particle filter-based tracking framework. The proposed algorithm is based on a combination of democratic integration and layered sampling. It is capable of dealing with deficiencies of single features as well as partial occlusion using the very same dynamic fusion mechanism. A set of simple but fast cues is defined, which allow us to cope with limited computational resources. The system is capable of automatic track initialization by means of a dedicated attention tracker permanently scanning the surroundings. © 2008 Springer Berlin Heidelberg.