  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 recent deep 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.
