Kernelized convex hull for visual tracking

Abstract

In visual tracking, developing a robust appearance model is a challenging task due to variations of object appearances such as background clutter, illumination variation and partial occlusion. In existing tracking algorithms, a target candidate is represented by linear combinations of target templates. However, the relationship between a target candidate and the corresponding target templates is nonlinear because of appearance variations. In this paper, we propose a kernelized convex hull based target representation for visual tracking. Namely, a target is represented by a nonlinear combination of target templates in a mapped higher dimensional feature space. The convex hull model can covers the target appearances that do not appear in the target templates. Experimental results demonstrate the robustness and effectiveness of the proposed tracking algorithm against several state-of-the-art tracking algorithms.