A mean shift algorithm based on modified Parzen window for small target tracking

Abstract

This paper addresses the problem of small scale target tracking. The divided-by-zero problem in the weight computation of mean shift algorithm and its associated tracking interrupt problem are presented. To tackle these problems, the Parzen window density estimation method is modified to interpolate the histogram of the target candidate. Then the Kullback-Leibler distance is employed as a new similarity measure between the target model and the target candidate. Its corresponding weight computation and new location expressions are derived. On the basis of these works, we propose a new small target tracking algorithm using mean shift framework. The tracking experiments for real world video sequences show that the proposed algorithm can track the target successively and accurately. It can successfully track very small targets with only 6×12 pixels.