Multi-modal summarization of key events and top players in sports tournament videos

  • Authors:

  • Dian Tjondronegoro;Xiaohui Tao;Johannes Sasongko;Cher Han Lau

  • Affiliations:

  • Faculty of Science and Technology, Queensland University of Technology, 126 Margaret Street, Brisbane QLD 4000, Australia;Faculty of Science and Technology, Queensland University of Technology, 126 Margaret Street, Brisbane QLD 4000, Australia;Faculty of Science and Technology, Queensland University of Technology, 126 Margaret Street, Brisbane QLD 4000, Australia;Faculty of Science and Technology, Queensland University of Technology, 126 Margaret Street, Brisbane QLD 4000, Australia

  • Venue:
  • WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
  • Year:
  • 2011

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Abstract

To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information. Previous work has successfully extracted semantic from the time-stamped web match reports, which are synchronized with the video contents. However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments. This paper aims to address this limitation using a novel multimodal summarization framework that is based on sentiment analysis and players' popularity. It uses audiovisual contents, web articles, blogs, and commentators' speech to automatically annotate and visualize the key events and key players in a sports tournament coverage. The experimental results demonstrate that the automatically generated video summaries are aligned with the events identified from the official website match reports.