Exciting event detection in broadcast soccer video with mid-level description and incremental learning

  • Authors:

  • Qixiang Ye;Qingming Huang;Wen Gao;Shuqiang Jiang

  • Affiliations:

  • Institute of Computing Technology of Chinese Academy of Sciences, Beijing, China;Graduate School of Chinese Academy of Sciences, Beijing, China;Graduate School of Chinese Academy of Sciences, Beijing, China;Graduate School of Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 13th annual ACM international conference on Multimedia
  • Year:
  • 2005

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Abstract

In this paper, we propose a method for exciting event detection in broadcast soccer video with mid-level description and SVM-based incremental learning. In the method, video frames are firstly classified and grouped into views in terms of low-level playfield features. Mid-level description including view label, motion descriptor and shot descriptor are then extracted to present the characteristics of a view. By using the fixed temporal structure of views, SVM classification models are constructed to detected exciting events in a soccer match. In the view classification and event detection procedures, SVM-based incremental learning method is explored to improve the extensibility of view classification and event detection. Experiments on real soccer video programs demonstrate encouraging results.