Shape based appearance model for kernel tracking

摘要

This paper investigates kernel based tracking using shape information. A kernel based tracker typically models an object with a primitive geometric shape, and then estimates the object state by fitting the kernel such that the appearance model is optimized. Most of the appearance models in kernel based tracking utilize the textural information within the kernel, although a few of them also make use of the gradient information along the kernel boundary. Interestingly, shape information of a general form has never been fully exploited in kernel tracking, despite the fact that shape has been widely used in silhouette tracking at the cost of intensive computation. In this paper, we propose an original way to incorporate shape knowledge into the appearance model of kernel based trackers while preserving their computational advantage versus silhouette based trackers. Experimental results demonstrate that kernel tracking is strongly improved by exploiting the proposed shape cue through comparisons to both kernel and silhouette trackers.