An Occlusion Robust Likelihood Integration Method for Multi-Camera People Head Tracking

Abstract

This paper presents a novel method for human head tracking using multiple cameras. Most existing methods estimate 3D target position according to 2D tracking results at different viewpoints. This framework can be easily affected by the inconsistent tracking results on 2D images, which leads 3D tracking failure. For solving this problem, an extension of Condensation using multiple images has been proposed. The method generates many hypotheses on a target (human head) in 3D space and estimates the likelihood of each hypothesis by integrating viewpoint dependent likelihood values of 2D hypotheses projected onto image planes. In theory, viewpoint dependent likelihood values should be integrated by multiplication, however, it is easily affected by occlusions. Thus we investigate this problem and propose a novel likelihood integration method in this paper and implemented a prototype system consisting of six sets of a PC and a camera. We confirmed the robustness against occlusions.