摘要
针对无人机视频中存在目标密集、运动噪声强而导致跟踪性能显著下降的问题,提出了一种改进YOLOv3的车辆检测算法及一种基于深度度量学习的多车辆跟踪算法。针对车辆检测的精度与实时性问题,采用深度可分离卷积网络MobileNetv3作为特征提取网络实现网络结构轻量化,同时采用CIoU Loss作为边框损失函数对网络进行训练。为了在多目标跟踪过程中提取到更具判别力的深度特征,提出了一种基于深度度量学习的多车辆跟踪算法,实验证明,本文提出的算法有效改善车辆ID跳变问题,速度上满足无人机交通视频下车辆跟踪的实时性要求,达到17 f/s。
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.
作者
胡硕
王洁
孙妍
周思恩
姚美玉
HU Shuo;WANG Jie;SUN Yan;ZHOU Sien;YAO Meiyu(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China)
出处
《智能系统学报》
CSCD
北大核心
2022年第4期798-805,共8页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(62073279).
作者简介
通信作者:胡硕,讲师,主要研究方向为模式识别、机器学习、机器视觉。E-mail:hus@ysu.edu.cn;王洁,硕士研究生,主要研究方向为机器学习、目标跟踪;孙妍,硕士研究生,主要研究方向为机器视觉、目标跟踪。
