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

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The Design of Low Noise Oscillatorsage. Belief propagation over a spatio-temporal graph of candidate body part hypotheses is used to estimate a temporally consistent pose between user-defined keyframe constraints. Experimental results show that the proposed generative pose estimation framework is capable of estimating pose even in very challenging unconstrained scenarios.

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Book 2014in 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

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2191-6586 tracking, player tracking and pose estimation, and the detecThe 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 intro

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https://doi.org/10.1007/BFb0017806enges, and propose a layered data association algorithm for tracking multiple tennis balls fully automatically. The effectiveness of the proposed algorithm is demonstrated on two data sets with more than 100 sequences from real-world tennis videos, where other data association methods perform poorly or fail completely.

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Ball Tracking for Tennis Video Annotationenges, and propose a layered data association algorithm for tracking multiple tennis balls fully automatically. The effectiveness of the proposed algorithm is demonstrated on two data sets with more than 100 sequences from real-world tennis videos, where other data association methods perform poorly or fail completely.

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Real-Time Event Detection in Field Sport Videosother exciting parts of a game that do not result in a score. The results obtained across a diverse dataset of different field sports are promising, demonstrating over 90 % accuracy for a feature-based event detector and 100 % accuracy for a scoreboard-based detector detecting only scores.

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https://doi.org/10.1007/978-3-319-09396-3Computer Vision; Human Activity and Behavior; Image and Video Analysis; Machine Learning; People Detecti