Tracking the players and the ball in team sports is key to analyse the
performance or to enhance the game watching experience with augmented reality.
When the only sources for this data are broadcast videos, sports-field
registration systems are required to estimate the homography and re-project the
ball or the players from the image space to the field space. This paper
describes a new basketball court registration framework in the context of the
MMSports 2022 camera calibration challenge. The method is based on the
estimation by an encoder-decoder network of the positions of keypoints sampled
with perspective-aware constraints. The regression of the basket positions and
heavy data augmentation techniques make the model robust to different arenas.
Ablation studies show the positive effects of our contributions on the
challenge test set. Our method divides the mean squared error by 4.7 compared
to the challenge baseline.

本文提出了一种新的篮球场注册框架，该框架基于编码器解码器网络的估计，采用透视感知约束对采样的关键点位置进行估计，并借助篮筐位置的回归和数据增强技术使模型对不同场馆具有鲁棒性。消融研究显示了我们的贡献对挑战测试集的积极影响，我们的方法将平均均方误差除以 4.7，比挑战基线提高了效果。