Abstract
In this work, we propose a novel way of efficiently localizing a sports 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 semantic cues such
as the field surface, lines and circles obtained from a deep
semantic segmentation network. Moreover, our approach is
fully automatic and depends only on a single image from
the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to soccer and hockey