In the application of video content an alyses and multimedia retrieval,person re-identification is a critical technique,which has great significance in the field of criminal inves tigation.This paper proposes a person re-identification algorithm based on HSV model and keyp oints matching.It first utilizes HSV model to pre-test pedestrian images and quickly rule out images whose main colors are different from the target,and then tests remaining images by matching keypoints.Then,the method of pre-testing pedestrian images compa res main colors of the torso and legs of two images according to an improved color quantization strategy of H SV space,which achieves better pre-testing effects and raises recognition speed of the algorithm.The method of keypoints matching first takes advantage of circularly symmetrical Gabor filters to generate multi-scale images,then extracts and des cribes keypoints by improved FAST and BRIEF algorithms,matches and purifies keypoints by Brute force algorithm and Ra ndom sample consensus algorithm, which achieves better matching effects than SIFT,SURF and ORB algorithms.For tes ting the validity of the proposed algorithm,we establish a pedestrian image library including 600images.Ex perimental results show that the proposed algorithm can identify pedestrian targets accurately by the speed of 12frames per second.