  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.
