by Michael Beetz, Jan Bandouch, Suat Gedikli, Nico von Hoyningen-Huene, Bernhard Kirchlechner and Alexis Maldonado
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.
Reference:
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.
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},
url = {https://ai.uni-bremen.de/papers/bee06camera.pdf},
bib2html_pubtype ={Refereed Conference Paper},
bib2html_rescat ={Game analysis},
bib2html_groups ={IAS, FIPM, Aspogamo},
bib2html_funding ={FIPM},
bib2html_domain = {Soccer Analysis},
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.}
}