Deep Learning for Visual Tracking: A Comprehensive Survey

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given ill-posed nature problem and its popularity a broad range real-world scenarios, number large-scale benchmark datasets have been established, on which considerable methods developed demonstrated with significant progress recent years -- predominantly by deep learning (DL)-based methods. This survey aims to systematically investigate current DL-based visual methods, datasets, evaluation metrics. It also extensively evaluates analyzes leading First, fundamental characteristics, primary motivations, contributions are summarized from nine key aspects of: network architecture, exploitation, training for tracking, objective, output, exploitation correlation filter advantages, aerial-view long-term online tracking. Second, popular benchmarks their respective properties compared, metrics summarized. Third, state-of-the-art comprehensively examined set well-established OTB2013, OTB2015, VOT2018, LaSOT, UAV123, UAVDT, VisDrone2019. Finally, conducting critical analyses these trackers quantitatively qualitatively, pros cons under various common scenarios investigated. may serve as gentle use guide practitioners weigh when what conditions choose method(s). facilitates discussion ongoing issues sheds light promising directions.