A dual-kernel-based tracking approach for visual target

Abstract

A dual-kernel-based tracking approach for visual target is proposed in this paper. The similarity between candidate and target model, and the contrast between candidate and its neighboring background are considered simultaneously when evaluating a target candidate. The similarity is measured by Bhattacharyya coefficient while the contrast is calculated with Jensen-Shannon divergence, and they are adaptively fused into a novel objective function. By maximizing the linear approximation of objective function, a dual-kernel target location-shift relation from current location to a new location is induced. According to the location-shift relation, the optimal target location can be recursively gained in the mean shift procedure. Experimental evaluations on several image sequences demonstrate that the proposed algorithm can gain more accurate target location and better identification power to false target, and it is also robust to deformation and partial occlusion.