  Unmanned aerial vehicles (UAV) have been widely used in various fields, and
their invasion of security and privacy has aroused social concern. Several
detection and tracking systems for UAVs have been introduced in recent years,
but most of them are based on radio frequency, radar, and other media. We
assume that the field of computer vision is mature enough to detect and track
invading UAVs. Thus we propose a visible light mode dataset called Dalian
University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains
a detection dataset with a total of 10,000 images and a tracking dataset with
20 videos that include short-term and long-term sequences. All frames and
images are manually annotated precisely. We use this dataset to train several
existing detection algorithms and evaluate the algorithms' performance. Several
tracking methods are also tested on our tracking dataset. Furthermore, we
propose a clear and simple tracking algorithm combined with detection that
inherits the detector's high precision. Extensive experiments show that the
tracking performance is improved considerably after fusing detection, thus
providing a new attempt at UAV tracking using our dataset.The datasets and
results are publicly available at: https://github.com/wangdongdut/DUT-Anti-UAV
