Comprehensive survey of deep learning-based approaches for aerial visual tracking

Unmanned aerial vehicle (UAV) or aerial drone, video, and image analysis is a developing application that has attracted substantial academic interest in several computer vision-related fields. Visual target tracking is one of the most popular and difficult study areas in computer vision. Recent deep learning (DL)-based approaches have resulted in many methods being developed and presented. This study reviewed and investigated the possibilities of using drones and computer vision techniques to undertake visual tracking in aerial video applications. Additionally, benchmark datasets, assessment criteria, and contemporary deep learning-based visual raking approaches are studied. This study aims to assess current accomplishments, emphasize the disadvantages and benefits of various existing approaches in each module, and deal with pressing research questions and difficult problems using visual tracking techniques. Finally, the direction for the next research is addressed, and the research foundation of this work is presented in aerial visual tracking.