Combining edge and color features for tracking partially occluded humans

Abstract

We propose an efficient approach for tracking humans in presence of severe occlusions through a combination of edge and color features. We implement a part based tracking paradigm to localize, accurately, the head, torso and the legs of a human target in successive frames. The Non-parametric color probability density estimates of these parts of the target are used to track them independently using mean shift. A robust edge matching algorithm, then, validates and refines the mean shift estimate of each part. The part based implementation of mean shift along with the novel edge matching algorithm ensures a reliable tracking of humans in upright pose through severe scene as well as inter-object occlusions. We use the CAVIAR Data Set as well as our own IIT Kanpur test cases demonstrating varying levels of occlusion in daily life situations to evaluate our tracking method. © Springer-Verlag 2010.