To solve the invalid tracking of a human target caused by appearance variations due to large angle change of the target in a robot mobile tracking, a multi-template regression weighted mean-shift algorithm was proposed. The algorithm could implement the target tracking by building a multi-template model of the target and applying mean shift. Firstly, the template set was obtained according to the results from mean shift procedure of the last frame and the coarse location information of head-shoulder model of a current frame, by which the position and angle variation of the target person were included. Then, the multi-template regression weighted mean-shift algorithm was used to determine the precise location of the target person. The regression model was introduced to multi-template mean shift to implement a map from color-texture feature to the similarity of target model to limit the number of templates and to ensure the real-time performance of the target detection. Finally, the proposed algorithm was verified by videos and robot tracking tests. The results show that the image average treatment time is 86.4 ms/frame, which satisfies the requirement of person tracking for a mobile robot. The method solves the appearance variation problem of targets in tracking processing and improves the robustness of human targets to its feature variations.