Soccer Field Localization from a Single Image
This provides an automatic solution for sports analysis without manual annotation or fixed cameras, potentially applicable to other sports like hockey and basketball.
The paper tackles the problem of localizing a soccer field from a single broadcast image by formulating it as branch and bound inference in a Markov random field using field cues like grass, lines, and circles, achieving promising results across various games.
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.