Chang Liu, Xiao-Fan Chen, Chun-Juan Bo, Dong Wang. Long-term Visual Tracking: Review and Experimental Comparison. Machine Intelligence Research, vol. 19, no. 6, pp.512-530, 2022. https://doi.org/10.1007/s11633-022-1344-1
Citation: Chang Liu, Xiao-Fan Chen, Chun-Juan Bo, Dong Wang. Long-term Visual Tracking: Review and Experimental Comparison. Machine Intelligence Research, vol. 19, no. 6, pp.512-530, 2022. https://doi.org/10.1007/s11633-022-1344-1

Long-term Visual Tracking: Review and Experimental Comparison

doi: 10.1007/s11633-022-1344-1
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  • Author Bio:

    Chang Liu received the B. Eng. degree in communication engineering from Dalian University of Technology, China in 2019. She is currently a Ph. D. degree candidate in signal and information processing at School of Information and Communication Engineering, Dalian University of Technology, China. Her research direction is visual object tracking. E-mail: njx2019@mail.dlut.edu.cnORCID iD: 0000-0002-2018-7162

    Xiao-Fan Chen received the B. Eng. degree in computer science from Dalian University of Technology, China in 2017. She is currently a master student in signal and information processing at School of Information and Communication Engineering, Dalian University of Technology, China. Her research direction is visual object tracking.E-mail: chenxf@mail.dlut.edu.cn

    Chun-Juan Bo received the Ph. D. degree in signal and information processing from Dalian University of Technology, China in 2019. She is currently an associate professor with College of Information and Communication Engineering, Dalian Minzu University, China. Her research interests include image classification and object tracking.E-mail: bcj@dlnu.edu.cn

    Dong Wang received the B. Eng. degree in electronic information engineering and the Ph. D. degree in signal and information processing from Dalian University of Technology (DUT), China in 2008 and 2013, respectively. He is currently a full professor with School of Information and Communication Engineering, DUT, China. His research interests focuses on object detection and tracking. E-mail: wdice@dlut.edu.cn (Corresponding author)ORCID iD: 0000-0002-6976-4004

  • Received Date: 2022-01-27
  • Accepted Date: 2022-06-06
  • Publish Online: 2022-11-07
  • Publish Date: 2022-11-22
  • As a fundamental task in computer vision, visual object tracking has received much attention in recent years. Most studies focus on short-term visual tracking which addresses shorter videos and always-visible targets. However, long-term visual tracking is much closer to practical applications with more complicated challenges. There exists a longer duration such as minute-level or even hour-level in the long-term tracking task, and the task also needs to handle more frequent target disappearance and reappearance. In this paper, we provide a thorough review of long-term tracking, summarizing long-term tracking algorithms from two perspectives: framework architectures and utilization of intermediate tracking results. Then we provide a detailed description of existing benchmarks and corresponding evaluation protocols. Furthermore, we conduct extensive experiments and analyse the performance of trackers on six benchmarks: VOTLT2018, VOTLT2019 (2020/2021), OxUvA, LaSOT, TLP and the long-term subset of VTUAV-V. Finally, we discuss the future prospects from multiple perspectives, including algorithm design and benchmark construction. To our knowledge, this is the first comprehensive survey for long-term visual object tracking. The relevant content is available at https://github.com/wangdongdut/Long-term-Visual-Tracking.

     

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