Abstract
In this paper, a novel statistical framework is proposed for shot segmentation and classification. The proposed framework segments and classifies shots simultaneously using same difference features based on statistical inference. The task of shot segmentation and classification is taken as finding the most possible shot sequence given feature sequences, and it can be formulated by a conditional probability which can be divided into a shot sequence probability and a feature sequence probability. Shot sequence probability is derived from relations between adjacent shots by Bi-gram, and feature sequence probability is dependent on inherent character of shot modeled by HMM. Thus, the proposed framework segments shot considering the character of intra-shot to classify shot, while classifies shot considering character of inter-shot to segment shot, which obtain more accurate results. Experimental results on soccer and badminton videos are promising, and demonstrate the effectiveness of the proposed framework.
This research was supported by National Basic Research Program of China (973 Program, 2007CB311100), Beijing Science and Technology Planning Program of China (D0106008040291), and National High Technology and Research Development Program of China (863 Program, 2007AA01Z416).
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Yang, Y., Lin, S., Zhang, Y., Tang, S. (2007). Statistical Framework for Shot Segmentation and Classification in Sports Video. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_11
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DOI: https://doi.org/10.1007/978-3-540-76390-1_11
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