Abstract: The feature points of the moving target in the video can be tracked through optical flow algorithm. When the target exists a movement with a relatively large scale, it is difficult to meet the image consistency hypothesis of optical flow, which results in the loss of tracked feature points. Concerning this problem, a method of moving human feature points tracking based on Lucas-Kanade pyramidal optical flow algorithm was proposed. First, the moving region of the human was obtained by the difference between the consecutive frames .Then, some feature points of the start frame were detected with the SIFT algorithm. Finally, the feature points were tracked in the subsequent frames through the image pyramidal optical flow. The experimental results suggest that the algorithm performs well on the feature points tracking of large scale movement and the tracking accuracy is improved.