This chapter describes a real-time system for event detection in sports broadcasts. The approach presented is applicable to a wide range of field sports. Using two independent event detection approaches that work simultaneously, the system is capable of accurately detecting scores, near misses, and other 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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Publication Type = Book Section
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Status = Published
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Subject = Engineering: Signal processing
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Subject = Computer Science: Multimedia systems
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
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DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
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Subject = Engineering
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DCU Faculties and Centres = Research Initiatives and Centres
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
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Subject = Computer Science: Image processing
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Subject = Computer Science
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DCU Faculties and Centres = DCU Faculties and Schools
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Dublin City University ->
DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
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Dublin City University ->
Subject = Computer Science: Digital video
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Dublin City University ->
Subject = Computer Science: Machine learning
Noel E. O'Connor,
Aleksandra Swietlicka,
Kevin McGuinness,
Rafal Kapela