Improved mean shift algorithm based on a dual patterns merging Robinson guard filter

Abstract

Mean shift, which is widely used in many target tracking systems, is a very effective algorithm to track the target. But the traditional mean shift tracking algorithm is limited to track an infrared small target. In infrared prewarning and tracking systems, the traditional mean shift tracking algorithm cannot achieve accurate tracking result due to that the target is weakened and submerged in the background noise. So in this paper, a compositive mean shift algorithm is put forward. In this algorithm, firstly on the basis of background suppression and division, noise is suppressed by an extraordinary Robinson Guard Filter. This paper adopts a dual patterns merging Robinson Guard Filter which is different from the traditional Robinson Guard Filter. According to the point target’s anisotropic singularity in space, this dual patterns merging Robinson Guard Filter can divide the direction further and detect singularity accurately in different directions in order to obtain better effect. The dual patterns merging Robinson Guard Filter’s improvement is that it adopts the horizontal and vertical direction window and the diagonal direction window whose protective belt width are both two at the same time to increase the probability of point target detection. The filter separately detects the two directions and merges the results in order to boost the effect of keeping back the details of the target. At the same time, it can also boost the effect of background suppression as much as possible and reduce the false alarm rate. At last the system can achieve ideal detection performance. After filtering, an image in which the point target and the background are distinguished is acquired. Then in the mean shift algorithm, we use the acquired image for target tracking. The results of experiment show that this improved mean shift algorithm can reduce failure probability of prewarning and track infrared small targets steadily and accurately.