The existing deterministic data association defines the optimal track set by global optimization algorithms, which can only work with prior knowledge of the objects number. Therefore, it is limited to vehicle detection in driver assistance system. A local optimization algorithm is proposed to implement deterministic correspondence in vehicle detection. With the aid of a proper tracker management strategy, the algorithm can handle object entries, object exits and occlusions. To improve the accuracy of correspondence, the cost function is defined by fusing multiple features. The motion feature and the figuration features are taken as main constraint condition and minor ones respectively. The validity of the tracking algorithm in vehicle detection system is verified by experiments.