Sports Field Localization via Deep Structured Models

Namdar Homayounfar, Sanja Fidler, Raquel Urtasun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5212-5220

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Homayounfar_2017_CVPR,
author = {Homayounfar, Namdar and Fidler, Sanja and Urtasun, Raquel},
title = {Sports Field Localization via Deep Structured Models},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}