Dogfight: Detecting Drones from Drones Videos

  title={Dogfight: Detecting Drones from Drones Videos},
  author={Muhammad Waseem Ashraf and Waqas Sultani and Mubarak Shah},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
As airborne vehicles are becoming more autonomous and ubiquitous, it has become vital to develop the capability to detect the objects in their surroundings. This paper attempts to address the problem of drones detection from other flying drones. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging. In this scenario, region-proposal based methods are not able to capture sufficient… Expand


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and C
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