In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. In contrast, we formulate this problem as a branch and bound inference in a Markov random field where an energy function is defined in terms of field cues such as grass, lines and circles. Moreover, our approach is fully automatic and depends only on single images from the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to various games and obtain promising results. Finally, we posit that our approach can be applied easily to other sports such as hockey and basketball.
In this paper we present ASPOGAMO, a vision system capable of es- timating motion trajectories of soccer players taped on video. The system per- forms well in a multitude of application scenarios because of its adaptivity to various camera setups, such as single or multiple camera settings, static or dy- namic ones. Furthermore, ASPOGAMO can directly process image streams taken from TV broadcast, and extract all valuable information despite scene interrup- tions and cuts between different cameras. The system achieves a high level of robustness through the use of modelbased vision algorithms for camera estima- tion and player recognition and a probabilistic multi-player tracking framework capable of dealing with occlusion situations typical in team-sports. The continu- ous interplay between these submodules is adding to both the reliability and the efficiency of the overall system.