Statistical framework for shot segmentation and classification in sports video

  • Authors:

  • Ying Yang;Shouxun Lin;Yongdong Zhang;Sheng Tang

  • Affiliations:

  • Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Beijing ...;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
  • Year:
  • 2007

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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.