Calibrating sports cameras is important for autonomous broadcasting and
sports analysis. Here we propose a highly automatic method for calibrating
sports cameras from a single image using synthetic data. First, we develop a
novel camera pose engine. The camera pose engine has only three significant
free parameters so that it can effectively generate a lot of camera poses and
corresponding edge (i.e, field marking) images. Then, we learn compact deep
features via a siamese network from paired edge image and camera pose and build
a feature-pose database. After that, we use a novel two-GAN (generative
adversarial network) model to detect field markings in real images. Finally, we
query an initial camera pose from the feature-pose database and refine camera
poses using truncated distance images. We evaluate our method on both synthetic
and real data. Our method not only demonstrates the robustness on the synthetic
data but also achieves the state-of-the-art accuracy on a standard soccer
dataset and very high performance on a volleyball dataset.

通过使用合成数据，我们提出了一种高度自动化的方法来通过单个图像校准运动相机，使用新颖的相机姿态引擎，以及采用两个 GAN 模型来检测场地标记，并在合成和真实数据中取得了鲁棒性和最新技术表现。