A framework for flexible summarization of racquet sports video using multiple modalities

  • Authors:

  • Chunxi Liu;Qingming Huang;Shuqiang Jiang;Liyuan Xing;Qixiang Ye;Wen Gao

  • Affiliations:

  • Graduate University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, PR China;Graduate University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, PR China and Key Lab of Intell. Info. Process., Inst. of Comput. Tech., Chinese Acade ...;Key Lab of Intell. Info. Process., Inst. of Comput. Tech., Chinese Academy of Sciences, No. 6, Kexueyuan South Road Zhongguancun, Haidian District, Beijing 100190, PR China;Centre of Quantifiable Quality of Service, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, Trondheim N-7491, Norway;Graduate University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, PR China;Peking University, No. 5, Summer Palace Road, Haidian District, Beijing 100871, PR China

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2009

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Abstract

While most existing sports video research focuses on detecting event from soccer and baseball etc., little work has been contributed to flexible content summarization on racquet sports video, e.g. tennis, table tennis etc. By taking advantages of the periodicity of video shot content and audio keywords in the racquet sports video, we propose a novel flexible video content summarization framework. Our approach combines the structure event detection method with the highlight ranking algorithm. Firstly, unsupervised shot clustering and supervised audio classification are performed to obtain the visual and audio mid-level patterns respectively. Then, a temporal voting scheme for structure event detection is proposed by utilizing the correspondence between audio and video content. Finally, by using the affective features extracted from the detected events, a linear highlight model is adopted to rank the detected events in terms of their exciting degrees. Experimental results show that the proposed approach is effective.