Midlevel cues mean shift visual tracking algorithm based on target-background saliency confidence map

Abstract

Because of the well-known merits of non-parametric estimation and fast mode matching, the mean shift tracking algorithm has been proposed with demonstrated success. But the traditional mean shift utilizes center-weighted color histogram as the reference model which is susceptible to interference of background pixels, and that can result in the compromised tracking robustness. In order to solve this problem, we propose a midlevel cues mean shift visual tracking algorithm based on target-background confidence map saliency-weighted model. A discriminative appearance model based on superpixels is introduced, thereby it can facilitate a tracker to distinguish between target and background by different weights. This improved mean shift tracker is formulated by computing a target-background saliency confidence map and mean shift iteration, and then we can obtain the position in next frame. Experimental results demonstrate that the improved mean shift tracker is able to handle occlusion and recover it from tracking drifts, furthermore, the improved algorithm facilitates foreground object segmentation during tracking.