Abstract
A five layered, event driven hierarchical framework for generic sports video classification has been proposed in this paper. The top layer classifications are based on a few popular audio and video content analysis techniques like short-time energy and Zero Crossing Rate (ZCR) for audio and Hidden Markov Model (HMM) based techniques for video, using color and motion as features. The lower layer classifications are done by applying game specific rules to recognize major events of the game. The proposed framework has been successfully tested with cricket and football video sequences. The event-related classifications bring us a step closer to the ultimate goal of semantic classifications that would be ideally required for sports highlight generation.
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Kolekar, M.H., Sengupta, S. (2006). A Hierarchical Framework for Generic Sports Video Classification. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_63
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DOI: https://doi.org/10.1007/11612704_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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