Abstract: A new pedestrian tracking and recognizing method is presented aiming at solving the problems in tracking and accounting pedestrian flow in visible light, such as low accuracy in dividing moving object and poor recognizing effect. Firstly, the moving object is divided by combining the SODP with edge detection and measure according to the consistency of the moving objects in the visual flow. Secondly, the tracking feature vectors are extracted rapidly with rough sampling based on the pedestrian moving model and the part feature of moving object. And the satisfying accuracy and recognizing speed are obtained when a pattern recognizes moving objects with features such as projection ratio of moving object outline and figure factors, and with the moving object assorting tool based on artificial neural network. The method is used in real time tracking and accounting the pedestrian flow in large malls, and the practical test results indicate that satisfying effect is obtained in both processing efficiency and recognizing accuracy with this method. Moreover, it has good adaptability to external influence such as light of the test spot, shadow of pedestrian, and change of pedestrian flow.