Stereo-Based Multi-Person Tracking using Overlapping Silhouette Templates

Abstract

This paper describes a stereo-based person tracking method for a person following robot. Many previous works on person tracking use laser range finders which can provide very accurate range measurements. Stereo-based systems have also been popular, but most of them are not used for controlling a real robot. We previously developed a tracking method which uses depth templates of person shape applied to a dense depth image. The method, however, sometimes failed when complex occlusions occurred. In this paper, we propose an accurate, stable tracking method using overlapping silhouette templates which consider how persons overlap in the image. Moreover, we 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.