Abstract: To address the problem of missed detection in the case of weak illumination and occlusion in vehicle detection, proposed an improved DeepSort tracking algorithm based on the YOLOv4 algorithm. Firstly, the YOLOv4 algorithm is used to extract features from the input image to obtain the target information, then estimate and update the vehicle's track condition using the Kalman filtering algorithm, and finally the matching relationship between the detection frame and the prediction frame is processed using Hungarian matching algorithm. The GIOU value of the detection and prediction results is used as measurement parameters instead of IOU value. The matching performance of DeepSort tracking algorithm is improved by it. Comparing the effect of vehicle detection with the single detection algorithm and the one after adding the tracking algorithm, the results show that there are fewer missed detections after adding the tracking algorithm, the vehicle detection effect is improved, and the robust is enhanced, and the MOTA is increased by 7.55%, proving the effectiveness of the improved method.