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.