[1]HU Shuo,WANG Jie,SUN Yan,et al.Research on multi-vehicle tracking algorithm from the perspective of UAV[J].CAAI Transactions on Intelligent Systems,2022,17(4):798-805.[doi:10.11992/tis.202108014]
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Research on multi-vehicle tracking algorithm from the perspective of UAV

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 798-805 Column: 学术论文—机器感知与模式识别 Public date: 2022-07-05
Title:
Research on multi-vehicle tracking algorithm from the perspective of UAV
Author(s):
HU Shuo;  WANG Jie;  SUN Yan;  ZHOU Sien;  YAO Meiyu
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Keywords:
vehicle detection; object tracking; UAV video; feature extraction; lightweight network; deep feature; loss function; deep metric learning
CLC:
TP391
DOI:
10.11992/tis.202108014
Abstract:
Aiming at the decline of tracking performance suffering from dense targets and strong motion noise in UAV video, we propose a vehicle detection algorithm based on improved YOLOv3 and a multi-vehicle tracking algorithm based on deep metric learning. To improve the vehicle detection system’s accuracy and real-time performance, a deep separable convolution network, MobileNetv3, is adopted as the feature extraction network to realize a lightweight network structure, and the CIoU Loss is used as the frame loss function to train the network. A multi-vehicle tracking algorithm based on depth metric learning is proposed to extract more discriminative deep features during multi-target tracking. Experiments reveal that the algorithm proposed in this paper effectively improves the problem of target ID jump and meets the real-time requirement of vehicle tracking in UAV traffic video, reaching 17 FPS.
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