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Titlebook: Computer Vision in Sports; Thomas B. Moeslund,Graham Thomas,Adrian Hilton Book 2014 Springer International Publishing Switzerland 2014 Com

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書目名稱Computer Vision in Sports
編輯Thomas B. Moeslund,Graham Thomas,Adrian Hilton
視頻videohttp://file.papertrans.cn/235/234091/234091.mp4
概述The first book of its kind devoted to the emerging field of computer vision in sports.The definitive reference on this topic, covering ball tracking, player tracking and pose estimation, and the detec
叢書名稱Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Computer Vision in Sports;  Thomas B. Moeslund,Graham Thomas,Adrian Hilton Book 2014 Springer International Publishing Switzerland 2014 Com
描述The first book of its kind devoted to this topic, this comprehensive text/reference presents state-of-the-art research and reviews current challenges in the application of computer vision to problems in sports. Opening with a detailed introduction to the use of computer vision across the entire life-cycle of a sports event, the text then progresses to examine cutting-edge techniques for tracking the ball, obtaining the whereabouts and pose of the players, and identifying the sport being played from video footage. The work concludes by investigating a selection of systems for the automatic analysis and classification of sports play. The insights provided by this pioneering collection will be of great interest to researchers and practitioners involved in computer vision, sports analysis and media production.
出版日期Book 2014
關(guān)鍵詞Computer Vision; Human Activity and Behavior; Image and Video Analysis; Machine Learning; People Detecti
版次1
doihttps://doi.org/10.1007/978-3-319-09396-3
isbn_softcover978-3-319-37973-9
isbn_ebook978-3-319-09396-3Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightSpringer International Publishing Switzerland 2014
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Detecting and Tracking Sports Players with Random Forests and Context-Conditioned Motion Models degrees of freedom to represent the full motion diversity of each player and could be difficult to use in practice. Instead, we introduce a set of . extracted from noisy detection data to describe the current state of the match, such as how the players are spatially distributed. Our assumption is t
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Estimating Athlete Pose from Monocular TV Sports Footagerithms and probabilistic prior models based on learned measurements. Such algorithms face challenges in generalisation beyond the learned dataset. We propose an interactive model-based generative approach for estimating the human pose from uncalibrated monocular video in unconstrained sports TV foot
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