The invention discloses a vehicle tracking method based on 
machine learning and 
optical flow. A vehicle model is obtained through one-off off-line training and used for detecting vehicle block 
mass Blobs in a video flow in real time, bidirectional 
pyramid optical flow tracking is performed on calculation 
characteristic point sets of all the vehicle 
mass block Blobs, and results of 
optical flow tracking in the forward direction and the backward direction are analyzed and filtered, so that multiple targets are stably and accurately tracked to form vehicle tracks. According to the complete vehicle tracking solution, the vehicle tracking method based on 
machine learning and optical flow can be widely applied to the fields of intelligent traffic, electronic polices, 
video monitoring, unmanned driving and others; by the utilization of the tracking method, a user can solve the classic problems in an existing tracking 
algorithm well, the multiple targets, such as long-period vehicle staying, size scale changing, shadowing, local shielding and touching, can be stably and accurately tracked; particularly, the vehicle tracking method has the good effects under the conditions of 
severe weather, a low illumination level and a high noisy point.