  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce
and recreation. With this, monitoring the operation status of UAVs is crucially
important. In this work, we consider the task of tracking UAVs, providing rich
information such as location and trajectory. To facilitate research on this
topic, we propose a dataset, Anti-UAV, with more than 300 video pairs
containing over 580k manually annotated bounding boxes. The releasing of such a
large-scale dataset could be a useful initial step in research of tracking
UAVs. Furthermore, the advancement of addressing research challenges in
Anti-UAV can help the design of anti-UAV systems, leading to better
surveillance of UAVs. Besides, a novel approach named dual-flow semantic
consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow
across video sequences, the tracker learns more robust class-level semantic
information and obtains more discriminative instance-level features.
Experimental results demonstrate that Anti-UAV is very challenging, and the
proposed method can effectively improve the tracker's performance. The Anti-UAV
benchmark and the code of the proposed approach will be publicly available at
https://github.com/ucas-vg/Anti-UAV.
