In order to improve the real-time performance and reliability of multi-target data association in complex traffic scenarios， a fast multi-target vehicle tracking algorithm based on half suppressed fuzzy c-means clustering （HS-FCM） is thus proposed in this article. Firstly， the multi-target vehicle-tracking problem is described mathematically， and the spatial mapping relationship between the camera pixel coordinate system and the world coordinate system is established. Secondly， the fuzzy clustering approach based on fuzzy theory is employed to solve the plot-track association problem. The probability of a feasible joint event in the joint probability data association （JPDA） algorithm is indirectly calculated by solving the fuzzy membership function defined by the distance between a sample and its cluster center. The multi-objective state is filtered and estimated by the probability weighted fusion method. Thirdly， in the dense vehicle environment the measurement update is suppressed by adjusting Kalman gain reasonably to solve the problem of short-term target losing. The real vehicle test and simulation results validate the feasibility and effectiveness of the proposed fast multi-vehicle tracking algorithm.
