Christopher R. Wren, Alex P. Pentland

Abstract

This paper describes a real-time, fully-dynamic, 3-D person tracking system that is able to tolerate full (temporary) occlusions and whose performance is substantially unaffected by the presence of multiple people. The framework provides a mathematically concise formulation for incorporating a wide variety of physical constraints and probabilistic influences. The framework takes the form of a recursive filter that enables pixel-level, probabilistic processes to take advantage of the contextual knowledge encoded in the higher-level models. Results are shown that demonstrate both qualitative and quantitative gains in tracking performance.