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Long-term Visual Tracking: Review and Experimental Comparison

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Abstract

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/wangdong-dut/Long-term-Visual-Tracking.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 62176041 and 62022021), Joint Fund of Ministry of Education for Equipment Pre-research, China (No. 8091B032155), the Science and Technology Innovation Foundation of Dalian, China (No. 2020 JJ26GX036), and the Fundamental Research Funds for the Central Universities, China (No. DUT21LAB127).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Chang Liu, Xiao-Fan Chen, Chun-Juan Bo & Dong Wang

  2. Dalian Minzu University, Dalian, 116600, China

    Chun-Juan Bo

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  1. Chang Liu
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Correspondence to Dong Wang.

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Chang Liu received the B. Eng. degree in communication engineering from Dalian University of Technology, China in 2019. She is currently a Ph. D. 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.

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.

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.

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.

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Liu, C., Chen, XF., Bo, CJ. et al. Long-term Visual Tracking: Review and Experimental Comparison. Mach. Intell. Res. 19, 512–530 (2022). https://doi.org/10.1007/s11633-022-1344-1

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