2019/12/31 by Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan +1
Computer Science · Engineering · #Artificial intelligence #Benchmark (surveying) #BitTorrent tracker #Computer science #Data science #Deep learning #Eye tracking #Image Enhancement Techniques #Infrared Target Detection Methodologies #Machine learning #Popularity #Tracking (education) #Video Surveillance and Tracking Methods #Visualization #cs.CV #cs.LG #eess.IV
paper · pdf · doi:10.1109/tits.2020.3046478
Accepted Manuscript in IEEE Transactions on Intelligent Transportation Systems
arxiv created 2021/01/26 · arxiv updated 2021/01/27 · openalex publication_date 2021/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years – predominantly by recentdeep learning(DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental characteristics, primary motivations, and contributions of DL-based methods are summarized from nine key aspects of: network architecture, network exploitation, network training for visual tracking, network objective, network output, exploitation of correlation filter advantages, aerial-view tracking, long-term tracking, and online tracking. Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized. Third, the state-of-the-art DL-based methods are comprehensively examined on a set of well-established benchmarks of OTB2013, OTB2015, VOT2018, LaSOT, UAV123, UAVDT, and VisDrone2019. Finally, by conducting critical analyses of these state-of-the-art trackers quantitatively and qualitatively, their pros and cons under various common scenarios are investigated. It may serve as a gentle use guide for practitioners to weigh when and under what conditions to choose which method(s). It also facilitates a discussion on ongoing issues and sheds light on promising research directions.