To address the problems of accuracy and efficiency limitations in traditional methods of studying vehicle trajectories and accelerate the promotion of digital road traffic management, this paper proposes a vehicle trajectory extraction method at intersections based on multi-target tracking optimization. First, based on the YOLOv8s algorithm framework, a multi-branch convolution strategy was introduced and an image processing method combining standard convolution and depthwise separable convolution was designed to improve the robustness of the model to different scenes and maintain a stable frame rate. Then, the loss function of the DeepSORT algorithm is improved by accurately quantifying the angle difference and distance loss to increase the convergence speed of the model and the accuracy of handling irregular objects. Finally, the accurate extraction of vehicle trajectories is ensured by deriving the conversion relationship between the pixel coordinate system and the real-world coordinate system. The experimental results show that the improved model has improved mAP, recall rate and MOTA by 2.9%, 5.6% and 0.7% respectively compared with the original model, and the number of encoding transformations (IDS) has decreased by 64%. The frame rate can be kept stable during detection. And by deriving the conversion relationship between the pixel coordinate system and the real-world coordinate system, the vehicle"s trajectory information in the surveillance video can be accurately extracted. This provides methodological support for in-depth research on vehicle characteristics and road traffic risks, and has high practical application value.