@inproceedings{734ea7e964e64529a6ed97154a763ac5,
title = "Multi-target vehicle detection and tracking based on video",
abstract = "In order to realize the vehicle tracking on the traffic road, a multi-target vehicle detecting and tracking method K-YOLOv3 based on video tracking is proposed in combination with experiments. The algorithm is composed of vehicle detection, tracking, trajectory generation part. The focus of this paper is vehicle detection and tracking. K-YOLOv3 is an improvement on YOLOv3, which is combined with KCF to detect and track the target vehicle at the same time, and then the vehicle trajectory set is established to form the vehicle trajectory. Experimental results show that the detection accuracy of k-yolov3 algorithm is slightly higher than YOLOv3, while the detection speed is much faster than YOLOv3.",
keywords = "KCF, Object detection, Object recognition, YOLOv3",
author = "Kun Zhang and Hang Ren and Yongquan Wei and Jun Gong",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd Chinese Control and Decision Conference, CCDC 2020 ; Conference date: 22-08-2020 Through 24-08-2020",
year = "2020",
month = aug,
doi = "10.1109/CCDC49329.2020.9164621",
language = "英语",
series = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3317--3322",
booktitle = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",
address = "美国",
}