The relationships among different cues are established to improve the robustness of a tracking method.A simple but effective model is utilized to easily implement the tracking method. A motion-consistency constraint is proposed among objects represented by different cues.A chain-structure Markov random field is used to express the objects represented by different cues and the constraint among them. The tracking problem is converted into a simple optimization of the target function of a Markov random field. The cues used in the experiment are luminance histogram

oriented gradient histogram

and local binary pattern. The comparison between several state-of-the-art tracking methods and the proposed method on 15 video sequences shows the effectiveness of the latter.The proposed method has low position error and high tracking accuracy when an object is influenced by occlusion

motion blur

illumination changes

and clutter. A motion-consistency constraint enhances the relationships among different cues to a certain degree. Expressing the constraint and the objects represented by different cues through a chain-structure Markov random field improves the robustness of the tracking method and makes it easy to implement.