  Unmanned aerial vehicle (UAV) tracking is of great significance for a wide
range of applications, such as delivery and agriculture. Previous benchmarks in
this area mainly focused on small-scale tracking problems while ignoring the
amounts of data, types of data modalities, diversities of target categories and
scenarios, and evaluation protocols involved, greatly hiding the massive power
of deep UAV tracking. In this work, we propose WebUAV-3M, the largest public
UAV tracking benchmark to date, to facilitate both the development and
evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames
across 4,500 videos and offers 223 highly diverse target categories. Each video
is densely annotated with bounding boxes by an efficient and scalable
semiautomatic target annotation (SATA) pipeline. Importantly, to take advantage
of the complementary superiority of language and audio, we enrich WebUAV-3M by
innovatively providing both natural language specifications and audio
descriptions. We believe that such additions will greatly boost future research
in terms of exploring language features and audio cues for multimodal UAV
tracking. In addition, a fine-grained UAV tracking-under-scenario constraint
(UTUSC) evaluation protocol and seven challenging scenario subtest sets are
constructed to enable the community to develop, adapt and evaluate various
types of advanced trackers. We provide extensive evaluations and detailed
analyses of 43 representative trackers and envision future research directions
in the field of deep UAV tracking and beyond. The dataset, toolkits and
baseline results are available at \url{https://github.com/983632847/WebUAV-3M}.

Computer Vision and Pattern Recognition