  With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are used
to fuel numerous important applications in computer vision, delivering more
efficiency and convenience than surveillance cameras with fixed camera angle,
scale and view. However, very limited UAV datasets are proposed, and they focus
only on a specific task such as visual tracking or object detection in
relatively constrained scenarios. Consequently, it is of great importance to
develop an unconstrained UAV benchmark to boost related researches. In this
paper, we construct a new UAV benchmark focusing on complex scenarios with new
level challenges. Selected from 10 hours raw videos, about 80,000
representative frames are fully annotated with bounding boxes as well as up to
14 kinds of attributes (e.g., weather condition, flying altitude, camera view,
vehicle category, and occlusion) for three fundamental computer vision tasks:
object detection, single object tracking, and multiple object tracking. Then, a
detailed quantitative study is performed using most recent state-of-the-art
algorithms for each task. Experimental results show that the current
state-of-the-art methods perform relative worse on our dataset, due to the new
challenges appeared in UAV based real scenes, e.g., high density, small object,
and camera motion. To our knowledge, our work is the first time to explore such
issues in unconstrained scenes comprehensively.
