Object tracking based on foreground probability function

In irregular object tracking, the precise model is not available because of the pixels belong to background in the tracking box. A novel tracking method based on foreground probability function is proposed to solve the problem above. Firstly, the foreground probability function is constructed to calculate the foreground probability of each pixel in tracking box, according to the color distribution of the foreground and the background region nearby. A precise model is obtained because the background pixels are excluded. Secondly, the foreground probability map is introduced to the mean shift framework to track with the object. After a tracking unit is completed, the scale of the tracking box is adjusted according to the new foreground probability map. Finally, the Bhattacharyya coefficient of the adjacent frames is used to modify the object model adaptively. Experimental results show that the foreground probability function is effective to suppress the compactness of the background pixels in the tracking box. Compared with the conventional tracker, the proposed tracker is robust to the scale change, and needs less iteration, which meets real-time tracking condition. In the tracking test of video sequence with complex background, the new tracker works better then conventional mean shift algorithm.