Robust Object Tracking Using Joint Color-Texture Histogram

A novel object tracking algorithm is presented in this paper by using the joint colortexture histogram to represent a target and then applying it to the mean shift framework. Apart from the conventional color histogram features, the texture features of the object are also extracted by using the local binary pattern (LBP) technique to represent the object. The major uniform LBP patterns are exploited to form a mask for joint color-texture feature selection. Compared with the traditional color histogram based algorithms that use the whole target region for tracking, the proposed algorithm extracts effectively the edge and corner features in the target region, which characterize better and represent more robustly the target. The experimental results validate that the proposed method improves greatly the tracking accuracy and efficiency with fewer mean shift iterations than standard mean shift tracking. It can robustly track the target under complex scenes, such as similar target and background appearance, on which the traditional color based schemes may fail to track.