  Owing to effective and flexible data acquisition, unmanned aerial vehicle
(UAV) has recently become a hotspot across the fields of computer vision (CV)
and remote sensing (RS). Inspired by recent success of deep learning (DL), many
advanced object detection and tracking approaches have been widely applied to
various UAV-related tasks, such as environmental monitoring, precision
agriculture, traffic management. This paper provides a comprehensive survey on
the research progress and prospects of DL-based UAV object detection and
tracking methods. More specifically, we first outline the challenges,
statistics of existing methods, and provide solutions from the perspectives of
DL-based models in three research topics: object detection from the image,
object detection from the video, and object tracking from the video. Open
datasets related to UAV-dominated object detection and tracking are exhausted,
and four benchmark datasets are employed for performance evaluation using some
state-of-the-art methods. Finally, prospects and considerations for the future
work are discussed and summarized. It is expected that this survey can
facilitate those researchers who come from remote sensing field with an
overview of DL-based UAV object detection and tracking methods, along with some
thoughts on their further developments.
