A fast stereo-based multi-person tracking using an approximated likelihood map for overlapping silhouette templates

This paper describes a method of tracking multiple persons with occlusions using stereo. Many previous stereo-based systems track each person separately and do not explicitly handle such occlusions. We previously developed an accurate, stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. However, because the method uses a particle filter, a lot of processing time is needed for estimating each particle´s likelihood by comparing many templates with the image. In this paper, we propose a new method which can decrease the number of image comparison by using an approximated likelihood map based on kernel density estimation. Experimental results show that the proposed method is able to reduce the processing time greatly without dropping the tracking performance.