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With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are usedto fuel numerous important applications in computer vision, delivering moreefficiency and convenience than surveillance cameras with fixed camera angle,scale and view. However, very limited UAV datasets are proposed, and they focusonly on a specific task such as visual tracking or object detection inrelatively constrained scenarios. Consequently, it is of great importance todevelop an unconstrained UAV benchmark to boost related researches. In thispaper, we construct a new UAV benchmark focusing on complex scenarios with newlevel challenges. Selected from 10 hours raw videos, about 80,000representative frames are fully annotated with bounding boxes as well as up to14 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, adetailed quantitative study is performed using most recent state-of-the-artalgorithms for each task. Experimental results show that the currentstate-of-the-art methods perform relative worse on our dataset, due to the newchallenges 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 suchissues in unconstrained scenes comprehensively.

The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking