SURF feature detection method used in object tracking

Traditional Mean-shift tracking algorithm cannot adjust the tracking windows according to the scale and orientation change of the object during tracking and get accurate localization. This paper combines SURF feature detection with the Mean-shift tracking, which matches the SURF feature in target of current and previous frames, calculate their orientation and proportion of scale to realize a scale and orientation changing tracking algorithm. The algorithm builds a model to describe the motion of target and forecast the location of center, which will get a better initial point and reduce iterations. The experiment results show that the proposed algorithm can disposal the scale and orientation change of target and reduce iterations.