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Computer Vision in Sports by Adrian Hilton, Graham Thomas, Thomas B. Moeslund

By Adrian Hilton, Graham Thomas, Thomas B. Moeslund

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In: CVPR 30. Yu X, Sim C, Wang JR, Cheong L (2004) A trajectory-based ball detection and tracking algorithm in broadcast tennis video. ICIP 2:1049–1052 31. Zhu G, Huang Q, Xu C, Xing L (2007) Human behavior analysis for highlight ranking in broadcast racket sports video. IEEE Trans Multimed 9(6):1167–1182 Chapter 3 Plane Approximation-Based Approach for 3D Reconstruction of Ball Trajectory for Performance Analysis in Table Tennis Sho Tamaki and Hideo Saito Abstract A plane approximation-based approach for 3D reconstruction of ball trajectory is introduced, which was developed as a solution to the problem with conventional analysis in table tennis.

Expert Syst Appl 36:9907–9918 11. Isard M, Blake A (1998) Condensation—conditional density propagation for visual tracking. Int J Comput Vis 1(29):5–28 12. Kijak E, Gravier G, Oisel L, Gros P (2003) Audiovisual integration for tennis broadcast structuring. In: International workshop on content-based multimedia indexing 13. Kijak E, Gravier G, Gros P, Oisel L, Bimbot F (2003) Hmm based structuring of tennis videos using visual and audio cues. IEEE Int Conf Multimed Expo 3:309–312 14. Kittler J, Christmas W, Kostin A, Yan F, Kolonias I, Windridge D (2005) A memory architecture and contextual reasoning framework for cognitive vision.

Two independent approaches are employed for event detection using audio-visual feature and on-screen scoreboard-based analysis. To allow application across a wide range of sports the system avoids the use of sports-specific visual cues such as pitch colour commonly used in field sport analysis. In the first approach, audio-visual features are trained for different events such as goals and other exciting game play. Different learning approaches for event detection are evaluated and it is found that artificial neural networks and decision trees achieve good results with event detection accuracy of around 90 %.

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