典型文献
Long-term Visual Tracking:Review and Experimental Comparison
文献摘要:
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 dis-cuss 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 .
文献关键词:
中图分类号:
作者姓名:
Chang Liu;Xiao-Fan Chen;Chun-Juan Bo;Dong Wang
作者机构:
School of Information and Communication Engineering,Dalian University of Technology,Dalian 116024,China;Dalian Minzu University,Dalian 116600,China
文献出处:
引用格式:
[1]Chang Liu;Xiao-Fan Chen;Chun-Juan Bo;Dong Wang-.Long-term Visual Tracking:Review and Experimental Comparison)[J].机器智能研究(英文),2022(06):512-530
A类:
VOTLT2018,VOTLT2019,OxUvA,VTUAV
B类:
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AB值:
0.60799
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