by M Beetz, Nvon Hoyningen-Huene, B Kirchlechner, S Gedikli, F Siles and Mand LM Durus
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:
ASpoGAMo: Automated Sports Game Analysis Models (M Beetz, Nvon Hoyningen-Huene, B Kirchlechner, S Gedikli, F Siles and Mand LM Durus), In International Journal of Computer Science in Sport, volume 8, 2009.
Bibtex Entry:
@article{beetz_aspogamo:_2009,
author = {M Beetz and Nvon Hoyningen-Huene and B Kirchlechner and S Gedikli and F Siles and Mand LM Durus},
title = {{ASpoGAMo:} Automated Sports Game Analysis Models},
journal = {International Journal of Computer Science in Sport},
year = {2009},
volume = {8},
number = {1},
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.},
keywords = {soccer},
}