by S Gedikli, J Bandouch, Nvon Hoyningen-Huene, B Kirchlechner and M Beetz
Abstract:
In this paper we present ASpoGAMo, a vision system capable of estimating motion trajectories of soccer players taped on video. The system performs well in a multitude of application scenarios because of its adaptivity to various camera setups, such as single or multiple camera settings, static or dynamic ones. Furthermore, ASpoGAMo can directly process image streams taken from TV broadcast, and extract all valuable information despite scene interruptions and cuts between different cameras. The system achieves a high level of robustness through the use of modelbased vision algorithms for camera estimation and player recognition and a probabilistic multi-player tracking framework capable of dealing with occlusion situations typical in team-sports. The continuous interplay between these submodules is adding to both the reliability and the efficiency of the overall system.
Reference:
An Adaptive Vision System for Tracking Soccer Players from Variable Camera Settings (S Gedikli, J Bandouch, Nvon Hoyningen-Huene, B Kirchlechner and M Beetz), In Proceedings of the 5th International Conference on Computer Vision Systems (ICVS), 2007.
Bibtex Entry:
@inproceedings{gedikli_adaptive_2007,
author = {S Gedikli and J Bandouch and Nvon Hoyningen-Huene and B Kirchlechner and M Beetz},
title = {An Adaptive Vision System for Tracking Soccer Players from Variable
Camera Settings},
booktitle = {Proceedings of the 5th International Conference on Computer Vision
Systems ({ICVS)}},
year = {2007},
abstract = {In this paper we present {ASpoGAMo}, a vision system capable of estimating
motion trajectories of soccer players taped on video. The system
performs well in a multitude of application scenarios because of
its adaptivity to various camera setups, such as single or multiple
camera settings, static or dynamic ones. Furthermore, {ASpoGAMo}
can directly process image streams taken from {TV} broadcast, and
extract all valuable information despite scene interruptions and
cuts between different cameras. The system achieves a high level
of robustness through the use of modelbased vision algorithms for
camera estimation and player recognition and a probabilistic multi-player
tracking framework capable of dealing with occlusion situations typical
in team-sports. The continuous interplay between these submodules
is adding to both the reliability and the efficiency of the overall
system.},
keywords = {soccer},
}