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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Kijak, E., Gravier, G., Gros, P., Oisel, L., Bimbot, F.: Hmm based structuring of tennis videos using visual and audio cues. In: Proc. of Int. Conf. on Multimedia and Expo, vol. 3, pp. 309–312 (2003)
Assfalg, J., Bertini, M., Bimbo, A.D., Nunziati, W., Pala, P.: Detection and recognition of football highlights using hmm. In: 9th Int. Conf. on Electronics, Circuits and Systems, vol. 3, pp. 1059–1062 (2002)
Duan, L., Xu, M., Tian, Q., Xu, C.: Nonparametric color characterisation using mean shift. In: Proc. of 11th ACM Int. Conf. on Multimedia, pp. 243–246 (2003)
Xie, L., Chang, S.F., Divakaran, A., Sun, H.: Structure analysis of soccer video with hidden markov models. In: Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing (2002)
Baoxin, L., Pan, H., Sezan, I.: A general framework for sports video summarization with its application to soccer. In: Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 3, pp. 169–172 (2003)
Hanjalic, A.: Generic approach to highlights extraction from a sports video. In: Proc. of IEEE Int. Conf. on Image Processing, vol. 1, pp. 1–4 (2003)
Hsu, W., Kennedy, L., Huang, C.W., Chang, S.F., Lin, C.Y., Iyengar, G.: News video story segmentation using fusion of multi-level multi-modal features in trecvid 2003. In: Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 3, pp. 645–648 (2004)
Chaisorn, L., Chua, T.S., Lee, C.H.: The segmentation of news video into story units. In: Proc. of Int. Conf. on Multimedia and Expo, vol. 1, pp. 73–76 (2002)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of IEEE 77 (1989)
Kolekar, M.H., Sengupta, S.: Hierarchical structure for audio-video based semantic classification of sports video sequences. In: Proc of SPIE Int. Conf. on Visual Communications and Image Processing, Beijing, China, vol. 5960, pp. 401–409 (2005)
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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
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