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Michael Beetz, Jan Bandouch, Suat Gedikli, Nico von Hoyningen-Huene, Bernhard Kirchlechner, and Alexis Maldonado. Camera-based Observation of Football Games for Analyzing Multi-agent Activities. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006.

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Abstract

This paper describes a camera-based observation system for football games that is used for the automatic analysis of football games and reasoning about multi-agent activity. The observation system runs on video streams produced by cameras set up for TV broadcasting. The observation system achieves reliability and accuracy through various mechanisms for adaptation, probabilistic estimation, and exploiting domain constraints. It represents motions compactly and segments them into classified ball actions.

BibTeX Entry

@InProceedings{Bee06camera,
  AUTHOR =	 {Michael Beetz and Jan Bandouch and Suat Gedikli and
                  Nico von Hoyningen-Huene and Bernhard Kirchlechner
                  and Alexis Maldonado},
  TITLE =	 {Camera-based Observation of Football Games for
                  Analyzing Multi-agent Activities},
  booktitle =	 {Proceedings of the Fifth International Joint
                  Conference on Autonomous Agents and Multiagent
                  Systems (AAMAS)},
  year =	 {2006},
  bib2html_pubtype ={Refereed Conference Paper},
  bib2html_rescat ={},
  bib2html_groups ={IAS,FIPM},
  bib2html_funding ={FIPM},
  bib2html_keywords ={},
  abstract =	 {This paper describes a camera-based observation
                  system for football games that is used for the
                  automatic analysis of football games and reasoning
                  about multi-agent activity. The observation system
                  runs on video streams produced by cameras set up for
                  TV broadcasting. The observation system achieves
                  reliability and accuracy through various mechanisms
                  for adaptation, probabilistic estimation, and
                  exploiting domain constraints. It represents motions
                  compactly and segments them into classified ball
                  actions.}
}

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