ASpoGAMo: Automated Sports Game Analysis Models (bibtex)
by Michael Beetz, Nicolai von Hoyningen-Huene, Bernhard Kirchlechner, Suat Gedikli, Francisco Siles, Murat Durus, Martin Lames
Abstract:
We propose automated sport game models as a novel technical means for the analysis of team sport games. The basic idea is that automated sport game models are based on a conceptualization of key notions in such games and probabilistically derived from a set of previous games. In contrast to existing approaches, automated sport game models provide an analysis that is sensitive to their context and go beyond simple statistical aggregations allowing objective, transparent and meaningful concept definitions. Based on automatically gathered spatio-temporal data by a computer vision system, a model hierarchy is built bottom up, where context-sensitive concepts are instantiated by the application of machine learning techniques. We describe the current state of implementation of the ASpoGaMo system including its computer vision subsystem that realizes the idea of automated sport game models. Their usage is exemplified with an analysis of the final of the soccer World Cup 2006.
Reference:
Michael Beetz, Nicolai von Hoyningen-Huene, Bernhard Kirchlechner, Suat Gedikli, Francisco Siles, Murat Durus, Martin Lames, "ASpoGAMo: Automated Sports Game Analysis Models", In International Journal of Computer Science in Sport, vol. 8, no. 1, 2009.
Bibtex Entry:
@Article{beetz09ijcss,
    author = {Michael Beetz and Nicolai von Hoyningen-Huene and Bernhard Kirchlechner and Suat Gedikli and Francisco Siles and Murat Durus and Martin Lames},
    title = {{ASpoGAMo: Automated Sports Game Analysis Models}},
    journal = {International Journal of Computer Science in Sport},
    year = {2009},
    volume = {8},
    number = {1},
    bib2html_pubtype = {Journal},
    bib2html_rescat  = {Perception,Models,Representation},
    bib2html_groups  = {Aspogamo},
    bib2html_funding  = {ASpoGAMo},
    bib2html_domain = {Soccer Analysis},
    abstract = {We propose automated sport game models as a novel technical
            means for the analysis of team sport games. The basic idea is that
            automated sport game models are based on a conceptualization of key
            notions in such games and probabilistically derived from a
            set of previous games. In contrast to existing approaches, automated
            sport game models provide an analysis that is sensitive to their context
            and go beyond simple statistical aggregations allowing objective,
            transparent and meaningful concept definitions. Based on automatically gathered spatio-temporal data
            by a computer vision system, a model hierarchy is built bottom up, where
            context-sensitive concepts are instantiated by the application of machine learning techniques.

            We describe the current state of implementation of the
            ASpoGaMo system including its computer vision subsystem
            that realizes the idea of automated sport game
            models. Their usage is exemplified with an analysis of
            the final of the soccer World Cup 2006.
    }
}
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