基于YOLOv3和DeepSort的车流量检测

doi:10.3969/j.issn.1000-1158.2021.06.05

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Abstract In view of the shortcomings of the traditional multi-target tracking algorithm, such as low detection accuracy and poor robustness, according to the classic Tracking-By-Detection mode, a vehicle flow detection method based on YOLOv3 and DeepSort is proposed, which realizes the real-time monitoring and tracking of the end-to-end vehicle flow video in vehicle video monitoring. The video vehicle target is detected by deep learning YOLOv3 algorithm, and then the detected vehicle is tracked in real time by deep learning DeepSort algorithm. The experimental results show that the method has good detection effect on traffic flow when dealing with the influence of fast moving vehicles and ambient light, and the average accuracy is up to 94.7%. The end-to-end algorithm is feasible and effective, which is suitable for video batch processing.

Key words： metrology vehicle flow detection YOLOv3 algorithm DeepSort algorithm deep learning image processing

Received: 12 November 2019 Published: 23 June 2021

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TB96

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	Articles by authors
	CHEN Jia-qian
	JIN Xuan-hong
	WANG Wen-yuan
	LU Ying-jie

Cite this article:

CHEN Jia-qian,JIN Xuan-hong,WANG Wen-yuan, et al. Vehicle Flow Detection Based on YOLOv3 and DeepSort[J]. Acta Metrologica Sinica, 2021, 42(6): 718-723.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.06.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I6/718

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