1.School of Information Science and Engineering,Yanshan University, Qinhuangdao, Hebei 066004,China
2.Hebei Provincial Key Laboratory of Special Optical Fibers and Fiber Optic Sensing, Qinhuangdao, Hebei 066004, China
Abstract：In order to improve the vehicle multi-target tracking accuracy under the UAV vision platform, a UAV visual vehicle multi-target tracking algorithm that combines the improved YOLOv7 network with the optimized ByteTrack algorithm is proposed. Firstly, in view of the situation where the features of small targets are not obvious, the feature extraction ability of shallow semantic information of the YOLOv7 network is enhanced, and SIoU-Loss is used to optimize the coordinate loss function to speed up the convergence speed of the anchor frame.secondly, according to the vehicle motion characteristics, in based on the ByteTrack algorithm, the state vector of the Kalman filter algorithm is integrated into the acceleration information. finally, the effectiveness of the algorithm is verified on the VisDrone2021 data set. The experimental results indicate that the average detection accuracy of the improved YOLOv7 network is 3.2% higher than the original network, the accuracy of the tracking algorithm is 1.2% higher than the baseline algorithm, and the high-order tracking accuracy is improved by 2.9%.
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2024, Vol. 45 
