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.
作者简介: 陈佳倩(1996-),女,江苏泰州人,上海理工大学在读硕士研究生,主要研究方向为信号的获取与处理、视觉检测、图像处理技术。Email: 1539142681@qq. com
引用本文:
陈佳倩,金晅宏,王文远,陆莹洁. 基于YOLOv3和DeepSort的车流量检测[J]. 计量学报, 2021, 42(6): 718-723.
CHEN Jia-qian, JIN Xuan-hong, WANG Wen-yuan, LU Ying-jie. Vehicle Flow Detection Based on YOLOv3 and DeepSort. Acta Metrologica Sinica, 2021, 42(6): 718-723.
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2021, Vol. 42 
