To deal with the problem of occlusion and appearance changes in person tracking with a mobile robot, an algorithm based on patches-based-multi-cues representation is proposed. The algorithm segments the candidate sample and extracts the depth information, color histograms, texture histograms from each image slice for constructing the detector. A motion extended Kalman filter(MEKF) is obtained by considering the motion of the robot and target. As tracking evolves, the target’s size is adaptively adjusted according to the depth histogram. Furthermore, occlusion is identified by simultaneously detecting the depth features and appearance model. Experiments results show that the proposed approach has better robustness for dealing with the problem of scale changes, illumination variations, and occlusion.