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The proliferation of Unmanned Aerial Vehicles (UAVs) such as drones has caused serious security and privacy concerns in the recent past. Detecting drones is extremely challenging in conditions where the drones exactly resemble a bird or any other flying entity and are to be detected under low visibility conditions – during the night, in hilly regions, in populated urban areas, in dense forest areas and in most cases the drones that are exceedingly far off from the field of view (FoV) of the surveillance cameras. In such cases of detecting drones under low visibility, infrared imaging outperforms RGB imaging since the images in the former case are based on the differences of the temperature measurements and they help identify and track objects that are otherwise not visible in images from conventional cameras. Although there have been several breakthroughs in the development of sophisticated cameras and motion sensors, the surveillance videos acquired are often distorted due to external environmental factors. The challenge dataset consists of infrared images and videos of drones and other flying entities under different scenarios as stated above. Participants are given the task of identifying and tracking drones under extremely low visibility conditions, with various levels of distortions in surveillance videos and at the same time drawing conclusions on the position of the drones with respect to the FoV of the surveillance camera. The outcomes of the challenge would be published and the data would be made available for further research.
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