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.
翻译:跟踪球员和球队运动中的球员和球员是分析性能或提高游戏观看经验的关键,以扩大现实。当这种数据的唯一来源是播放视频时,要求体育场登记系统来估计同性,从图像空间到实地空间对球员或球员进行重新投射。本文介绍了在MMSports 2022 相机校准挑战背景下的一个新的篮球法庭登记框架。该方法基于一个编码器-解码器网络对通过透视-觉限制取样的关键点位置的估计。篮子位置的回归和重数据增强技术使模型对不同场的坚固。减法研究表明我们的贡献对挑战测试集的积极影响。我们的方法将平均平方差差比比挑战基线除以4.7。