A unified framework for semantic shot classification in sports videos

  • Authors:

  • Ling-Yu Duan;Min Xu;Xiao-Dong Yu;Qi Tian

  • Affiliations:

  • Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore;Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore;Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore;Laboratories for Information Technology, Agency for Science, Technology and Research, Singapore

  • Venue:
  • Proceedings of the tenth ACM international conference on Multimedia
  • Year:
  • 2002

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Abstract

In this demonstration, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of specific sport to perform a top-down video shot classification, including identification of video shots classes for each sport, and supervised learning and classification of given sports video with low-level and middle-level features extracted from the sports video. It's observed that for each sport we can predefine a small number of semantic shot classes, 5--10, which cover 90 to 95 % of sports broadcasting video. With supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot attributes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 3 types of sports videos: tennis, basketball, and soccer. Good classification results ranging from 80~95% have been achieved. The proposed framework provides a generic solution for sports video semantic shot classification, which can be adapted to a new sport type easily. With correctly classified sports video shots further structural and temporal analysis will be greatly facilitated.