Research on Multi-target Vehicle Tracking Based on Improved DeepSORT Algorithm
In order to solve the problems of low accuracy of DeepSORT tracking algorithm of real-time traffic monitoring video in traditional multi-target vehicle tracking,and the decrease of vehicle tracking accuracy due to large vehicle occlusion area in the video,an end-to-end YOLO v8 detection method is proposed to replace the traditional YOLO v8 detection method.At the same time,DeepSORT data association integrates the motion state and appearance attributes of the target,and then combines the static characteristics,motion trajectory and appearance details of the vehicle through Kalman filter,and dynamically adjusts their weights according to the ambient lighting conditions to optimize the tracking performance in low illumination.The experimental results show that compared with the traditional algorithm,the algorithm proposed in this paper can deal with occlusion and differ-ent lighting conditions well,and its accuracy and speed are also better than the traditional multi-target vehicle tracking detection algorithm.
YOLO v8Kalman filtermulti-target trackingdeepSORTdata association
NETL
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