Visual tracking by partition-based histogram backprojection and maximum support criteria

Histogram-based mean-shift is an efficient tool for visual object tracking. However, it often fails to locate a target object correctly in complex environment especially when the background contains similar colors with the object. In this paper, we present a novel visual tracking method that combines advantages of real-time performance of the mean-shift and exact localization of template matching and is robust to background changes, partial occlusions, and pose changes. The proposed method uses a partition-based object model represented by multiple patch histograms. The method first estimates the densities of the object pixels by histogram backprojection of each patch histogram, which gives a set of patch-wise density estimates. A target object is then located by pixel-wise evaluation of the maximum likelihood which is computed by the sum of the densities of the object pixels within target candidate. The suggested localization criteria overcomes many problems of the conventional mean-shift and gives significant improvement of tracking performance. The proposed tracker is very fast and the tracking accuracy is comparable to recent state-of-the-art trackers. The experiment on extensive challenging video sequences confirms the efficiency of our method.