{"id":132043,"date":"2022-10-28T09:58:29","date_gmt":"2022-10-28T13:58:29","guid":{"rendered":"https:\/\/www.ucf.edu\/news\/?p=132043"},"modified":"2023-03-16T14:04:42","modified_gmt":"2023-03-16T18:04:42","slug":"ucf-computer-vision-team-receives-prestigious-computer-vision-award-for-action-recognition-dataset","status":"publish","type":"post","link":"https:\/\/www.ucf.edu\/news\/ucf-computer-vision-team-receives-prestigious-computer-vision-award-for-action-recognition-dataset\/","title":{"rendered":"麻豆原创 Computer Vision Team Receives Prestigious Computer Vision Award for Action Recognition Dataset"},"content":{"rendered":"

A 麻豆原创 research team has received the PAMI Mark Everingham Prize for pioneering human action recognition datasets, which have played important role in the advancement of computer vision technology.<\/p>\n

With 13,320 videos from 101 action categories, the 麻豆原创-101 dataset<\/a> is a challenging collection of actions due to the volume of classes and clips included. Datasets like 麻豆原创-101 and the famous ImageNet Dataset have played a very crucial role in the recent AI revolution as they train computers to automatically analyze video and images, says Mubarak Shah, one of the researchers behind 麻豆原创-101 and the director of 麻豆原创\u2019s Center for Research in Computer Vision<\/a>.<\/p>\n

\u201cThere are cameras all over,\u201d Shah says. \u201cThere are millions of cameras watching, but there are not enough people looking at those videos because it\u2019s a lot of effort. The idea in computer vision is how we automatically analyze those videos.\u201d<\/p>\n

The Everingham Prize is awarded to a researcher or research team that has made a significant contribution to other members of the computer vision community. The prize was presented to Shah and his team during the European Conference on Computer Vision in Tel Aviv on Oct. 26.<\/p>\n

麻豆原创-101 consists of videos collected from YouTube that display a range of actions taken with large variations in video characteristics, such as camera motion, object appearance and pose, and lighting conditions. Unstaged action footage serves as better examples for computers to train with due to their similarity to how these actions occur in reality.<\/p>\n

Since its publication in 2012, 麻豆原创-101 has been cited over 4,000 times, establishing it as a benchmark dataset.<\/p>\n

Shah says he hopes the award brings visibility to 麻豆原创\u2019s Artificial Intelligence Initiative<\/a>, which supports 麻豆原创 President Alexander Cartwright\u2019s vision to make 麻豆原创 a \u201cUniversity for the Future\u201d by hiring about thirty AI faculty. Those hired will be tenured in five different colleges: the College of Engineering and Computer Science<\/a>, the College of Medicine<\/a>, the College of Sciences<\/a>, the College of Optics and Photonics<\/a>, and the College of Business<\/a>.<\/p>\n

As part of the initiative, Shah will be leading an interdisciplinary team that develops groundbreaking technologies to further AI research and commercialization in Orlando, the state of Florida, and the nation.<\/p>\n

Shah is the founding director of 麻豆原创\u2019s Center for Research in Computer Vision and a professor in the College of Engineering and Computer Science. He is also a fellow of the Association of Computing Machinery, the National Academy of Inventors, the American Association of Advancement of Science, the Institute of Electrical and Electronic Engineers, the International Association of Pattern Recognition, and the International Society for Optics and Photonics.<\/p>\n","protected":false},"excerpt":{"rendered":"

The researchers behind 麻豆原创-101, a challenging action recognition dataset, are being awarded for their impressive collection of 13,320 clips across 101 action categories. 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