[1]胡硕,王洁,孙妍,等.无人机视角下的多车辆跟踪算法研究[J].智能系统学报,2022,17(4):798-805.[doi:10.11992/tis.202108014]
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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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]
无人机视角下的多车辆跟踪算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
798-805
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-07-05
- Title:
- Research on multi-vehicle tracking algorithm from the perspective of UAV
- Keywords:
- vehicle detection; object tracking; UAV video; feature extraction; lightweight network; deep feature; loss function; deep metric learning
- 分类号:
- TP391
- 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。
- 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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备注/Memo
收稿日期:2021-08-14。
基金项目:国家自然科学基金项目(62073279).
作者简介:胡硕，讲师，主要研究方向为模式识别、机器学习、机器视觉;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
通讯作者:胡硕. E-mail: hus@ysu.edu.cn
更新日期/Last Update:
1900-01-01