基于YOLOv3与分层数据关联的多目标跟踪算法
计算机科学

计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 370-375.doi: 10.11896/jsjkx.201000115

• 图像处理& 多媒体技术 • 上一篇    下一篇

基于YOLOv3与分层数据关联的多目标跟踪算法

刘彦1,2, 秦品乐1, 曾建朝1,2   

  1. 1 中北大学山西省医学影像人工智能工程技术研究中心 太原030051
    2 中北大学电气与控制工程学院 太原030051
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 曾建朝(zjc@nuc.edu.cn)
  • 作者简介:ly950322@126.com
  • 基金资助:
    山西省重点研发项目(201803D31212-1)

Multi-object Tracking Algorithm Based on YOLOv3 and Hierarchical Data Association

LIU Yan1,2, QIN Pin-le1, ZENG Jian-chao1,2   

  1. 1 Shanxi Medical Imaging and Data Analysis Engineering Research Center,North University of China,Taiyuan 030051,China
    2 School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:LIU Yan,born in 1995,master's degree student,is a member of China ComputerFederation.Her main research interests include multi-target tracking,digitalimage processing,computer vision.
    ZENG Jian-chao,born in 1963,Ph.D,is a member of China Computer Federation.His main research interests include medical image and maintenance decision of complex system.
  • Supported by:
    Shanxi Provincial Key Research and Development Plan(201803D31212-1).

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摘要： 为了缓解多目标跟踪算法中实时性的问题以及在跟踪过程中目标由于外观相似度太高和误检数量过多而造成的跟踪困难问题,提出了一种多目标跟踪算法,该算法基于改进YOLOv3与分层数据关联。由于轻量级网络MobileNet使用了深度可分离卷积对原有网络进行压缩,达到了减少网络参数的目的,因此文中在保留YOLOv3网络多尺度预测部分的情况下,利用MobileNet替换YOLOv3网络的主体结构,实现降低网络的复杂度,使算法达到实时的要求。与其他多目标跟踪算法中使用的检测网络相比,该算法提出的检测网络模型的大小为91 M,而单张检测时间可以达到3.12 s。同时,该算法引入基于目标外观特征和运动特征的分层数据关联方法。与仅使用外观特征进行关联的方法相比,分层数据关联方法使得算法的评价指标MOTA提升6.5%,MOTP提升1.7%。在MOT16数据集上跟踪精度可以达到77.2%,同时具备良好的抗干扰能力与实时性。

关键词: YOLOv3, 多目标跟踪, 分层数据关联, 轻量级网络, 深度学习

Abstract: In order to alleviate the real-time problem of multi-object tracking methods and the tracking difficulty caused by the high similarity of appearance and the excessive number of error detection in the tracking process,a new multi-object tracking method is proposed,which is based on the improved YOLOv3 and hierarchical data association.As the lightweight network MobileNet uses the deep separable convolution to compress the original network,so as to reduce the network parameters,we uses MobileNet to replace the main structure of YOLOv3 network while retaining the multi-scale prediction part of YOLOv3,so as to reduce the complexity of the network and make the method meet the real-time requirements.Compared with the detection network used in other multi-object tracking methods,we proposed detection network model size is 91 M,and the single detection time can reach 3.12 s.At the same time,the algorithm introduces hierarchical data association method based on object appearance features and motion features.Compared with the method using only appearance features,the hierarchical data association method improves the evaluation index MOTA by 6.5 and MOTP by 1.7.On the MOT16 data set,the tracking accuracy can reach 77.2% and has good anti-jamming ability and real-time performance.

Key words: Deep learning, Hierarchical data association, Lightweight network, Multi-object tracking, YOLOv3

中图分类号: 

  • TP391.41
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