UG^2
Introduced by Vidal et al. in UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches.
Source: UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual RecognitionPapers
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