Abstract: A visual tracking algorithm based on binocular matching is proposed to solve the problems such as high error rate, low robustness and poor information fusion existing in current visual tracking methods under dynamic background. Based on the physical structure of binocular camera, the proposed algorithm adopts a closed quadrilateral method to match feature points so as to implement the binocular matching and 3D tracking and optimize the searching structure. The binocular images are filtered by Gauss-Laplace template, and the feature points extracted by the Harris corner detection algorithm are encapsulated orderly into feature descriptor. Then, the sum of absolute difference is used as the matching criteria, a 4-side searching criteria is set up for neighbor searching, and RANSAC(random sample consensus) algorithm is introduced for reliability screening. Finally, the accuracy of feature tracking is improved through the 4 points formed quadrilateral closed-loop detection. To evaluate the performance of the proposed method, images with different resolutions under different road conditions are collected, and test experiments are conducted. The experimental results show that the average tracking precision of the proposed method reaches 99.80%. The robustness and tracking accuracy of the proposed method is superior to the optical flow method.