A video-based multi-object vehicle tracking and real-time trajectory distribution algorithm is proposed to display the driving trajectories of vehicles in a highway traffic video, which can provide useful traffic information for traffic management and decision-making. Firstly, the YOLOv4 algorithm is used to detect vehicle objects. Secondly, in different traffic scenarios, the vehicle data is correlated to yield a complete trajectory by using the proposed tracking method based on sparse frame detection in combination with KCF tracking algorithm. Finally, the vehicle trajectory is displayed with the vehicle distribution map and the top view of traffic scenes, which is convenient for traffic management and analysis. Experimental results show that the proposed vehicle tracking method has an excellent tracking accuracy and a fast processing speed. The real-time trajectory distribution correctly reflects the lane information of real scenes and movement information of the object vehicles, which has a great application value.