Adaptive Bandwidth Mean Shift Object Tracking

Abstract

In this paper, a novel adaptive bandwidth mean shift algorithm toward 2D object tracking is proposed. It can simultaneously tracks the scale and orientation besides position in real time. The feature histogram weighted by a kernel with adaptive bandwidth is used for representing the target and the candidate target. The similarity of the target model and the candidate model is measured by the Bhattacharyya coefficient. A two step method is used iteratively to find the most probable target position, scale and orientation. The first step is to find the position using a mean shift iteration, the second step is to find the bandwidth which best describes the region of the object. Its convergence is proved theoretically. Experiments show that it can successfully track the position, scale and orientation in real time.