Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (7): 1504-1515.DOI: 10.3778/j.issn.1673-9418.2111105

• Surveys and Frontiers • Previous Articles     Next Articles

Survey on Video Object Tracking Algorithms

LIU Yi1,+(), LI Mengmeng1, ZHENG Qibin2, QIN Wei1, REN Xiaoguang1   

  1. 1. Defense Innovation Institute, Beijing 100071, China
    2. Academy of Military Science, Beijing 100091, China
  • Received:2021-11-22 Revised:2022-01-20 Online:2022-07-01 Published:2022-07-25
  • Supported by:
    the National Natural Science Foundation for Young Scientists of China(61802426)


刘艺1,+(), 李蒙蒙1, 郑奇斌2, 秦伟1, 任小广1   

  1. 1.国防科技创新研究院,北京 100071
    2.军事科学院,北京 100091
  • 作者简介:刘艺(1990—),男,安徽蚌埠人,博士,助理研究员,主要研究方向为机器人操作系统、数据质量、演化算法。
    LIU Yi, born in 1990, Ph.D., assistant researcher. His research interests include robot operating system, data quality and evolutionary algorithms.
    LI Mengmeng, born in 1992, M.S. candidate. Her research interests include evolutionary algorithms, data quality, object tracking, etc.
    ZHENG Qibin, born in 1990, Ph.D., assistant researcher. His research interests include data engineering, data mining, machine learning, etc.
    QIN Wei, born in 1983, M.S., assistant researcher. His research interest is intelligent information system management.
    REN Xiaoguang, born in 1986, Ph.D., associate research fellow. His research interests include high performance computing, numerical computation and simulation, robot operation systems, etc.
  • 基金资助:


Video object tracking is an important research content in the field of computer vision, mainly studying the tracking of objects with interest in video streams or image sequences. Video object tracking has been widely used in cameras and surveillance, driverless, precision guidance and other fields. Therefore, a comprehensive review on video object tracking algorithms is of great significance. Firstly, according to different sources of challenges, the challenges faced by video object tracking are classified into two aspects, the objects’ factors and the backgrounds’ factors, and summed up respectively. Secondly, the typical video object tracking algorithms in recent years are classified into correlation filtering video object tracking algorithms and deep learning video object tracking algorithms. And further the correlation filtering video object tracking algorithms are classified into three categories: kernel correlation filtering algorithms, scale adaptive correlation filtering algorithms and multi-feature fusion corre-lation filtering algorithms. The deep learning video object tracking algorithms are classified into two categories: video object tracking algorithms based on siamese network and based on convolutional neural network. This paper analyzes various algorithms from the aspects of research motivation, algorithm ideas, advantages and disadvantages. Then, the widely used datasets and evaluation indicators are introduced. Finally, this paper sums up the research and looks forward to the development trends of video object tracking in the future.

Key words: computer vision, video object tracking, correlation filtering, deep learning



关键词: 计算机视觉, 视频目标跟踪, 相关滤波, 深度学习

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