Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2718-2733.DOI: 10.3778/j.issn.1673-9418.2204041

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Deep Online Multi-object Tracking Algorithms

LIU Wenqiang, QIU Hangping(), LI Hang, YANG Li, LI Yang, MIAO Zhuang, LI Yi, ZHAO Xinxin   

  1. School of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
  • Received:2022-04-13 Revised:2022-07-13 Online:2022-12-01 Published:2022-12-16
  • About author:LIU Wenqiang, born in 1996, M.S. candidate. His research interests include machine vision and multi-object tracking.
    QIU Hangping, born in 1965, Ph.D., professor. Her research interests include systems engineering and information retrieval.
    LI Hang, born in 1983, Ph.D., engineer. His research interests include computer vision and information fusion.
    YANG Li, born in 1981, M.S. candidate. His research interests include machine vision and object detection.
    LI Yang, born in 1984, Ph.D., associate professor. His research interests include artificial intelligence and deep learning.
    MIAO Zhuang, born in 1976, Ph.D., associate professor. His research interests include image and video processing.
    LI Yi,born in 1998, M.S. candidate. His research interests include artificial intelligence and image fusion.
    ZHAO Xinxin, born in 1996, M.S. candidate. Her research interests include machine learning and image retrieval.
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20200581);Postdoctoral Science Foundation of China(2020M683754)


刘文强, 裘杭萍(), 李航, 杨利, 李阳, 苗壮, 李一, 赵昕昕   

  1. 陆军工程大学 指挥控制工程学院,南京 210007
  • 通讯作者: +E-mail:
  • 作者简介:刘文强(1996—),男,江西上饶人,硕士研究生,主要研究方向为机器视觉、多目标跟踪。
  • 基金资助:


Video multi-object tracking is a key task in the field of computer vision and has a wide application prospect in industry, commerce and military fields. At present, the rapid development of deep learning provides many solutions to solve the problem of multi-object tracking. However, the challenging problems such as mutation of target appearance, serious occlusion of target area, disappearance and appearance of target have not been completely solved. This paper focuses on online multi-object tracking algorithm based on deep learning, and summarizes the latest progress in this field. According to the three important modules of feature prediction, apparent feature extraction and data association, as will as the two frameworks of detection-based-tracking (DBT) and joint-detection-tracking (JDT), this paper divides deep online multi-object tracking algorithms into six sub-classes, and discusses the principles, advantages and disadvantages of different types of algorithms. Among them, the multi-stage design of the DBT algorithm has a clear structure and is easy to optimize, but multi-stage training may lead to sub-optimal solutions; the sub-modules of the JDT algorithm that integrates detection and tracking achieve faster inference speed, but there is a problem of collaborative training of each module. Currently, multi-target tracking begins to focus on long-term feature extraction of targets, occlusion target processing, association strategy improvement, and end-to-end framework design. Finally, combined with the existing algorithms, this paper summarizes urgent problems to be solved in deep online multi-object tracking and looks forward to possible research directions in the future.

Key words: online multi-object tracking, deep learning, feature extraction, data association



关键词: 在线多目标跟踪, 深度学习, 特征提取, 数据关联

CLC Number: