An adaptive fusion architecture for target tracking

Abstract

A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location. Bayesian probability theory provides the framework for sensor fusion, and resource scheduling is used to intelligently allocate the limited computational resources available across the suite of cues. The system is shown to track a person in 3D space moving in a cluttered environment.