基于无人机航拍视频车辆多目标跟踪算法研究

doi:10.3969/j.issn.1000-1158.2024.12.04

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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%.

Key words： computer vision image processing multi-target tracking;unmanned aerial vehicle YOLOv7 network ByteTrack algorithm vehicle detection

Received: 28 November 2023 Published: 18 December 2024

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	Articles by authors
	ZHU Qiguang
	SHANG Jian
	LIU Bo
	CEN Qiang
	CHEN Weidong

Cite this article:

ZHU Qiguang,SHANG Jian,LIU Bo, et al. Research on Vehicle Multi-target Tracking Algorithm Based on UAV Aerial Video[J]. Acta Metrologica Sinica, 2024, 45(12): 1772-1779.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.12.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I12/1772

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