Abstract: Harris corner points were adopted as tracking objects， and scale space was introduced into corner point detection in order to extract Harris corner points in feature scale. Then curvature was computed to filter out false corners and enhance adaptability to scale change. Optical flow method was adopted for the tracking algorithm based on image pyramid， in which the optical flow iteratively was computed. And the tracking algorithm based on the optical flow error was proposed. That is， the trajectory error in the same frame with different time flow was used to evaluate the tracking situation. In this way， tracking failure was avoided when the tracking object is hidden， disappears or textural features change. Experimental results of different video sequences show that the proposed optical flow tracking algorithm based on improved corner extraction and image pyramid has better tracking performances. The feature points could be filtered effectively that lead to tracking failure with the introduction of optical flow error method， and the object positions are estimated accurately.

Key words: object tracking, corner point, feature scale, optical flow, image pyramid