Chang Liu, Xiao-Fan Chen, Chun-Juan Bo, Dong Wang. Long-term Visual Tracking: Review and Experimental Comparison[J]. Machine Intelligence Research, 2022, 19(6): 512-530. DOI: 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[J]. Machine Intelligence Research, 2022, 19(6): 512-530. DOI: 10.1007/s11633-022-1344-1

Long-term Visual Tracking: Review and Experimental Comparison

  • 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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