In the field of computer vision-driven ice hockey analytics, one of the most challenging and least studied tasks is goalie pose estimation. Unlike general human pose estimation, goalie pose estimation is much more complex as it involves not only the detection of keypoints corresponding to the joints of the goalie concealed under thick padding and mask, but also a large number of non-human keypoints corresponding to the large leg pads and gloves worn, the stick, as well as the hockey net. To tackle this challenge, we introduce GoalieNet, a multi-stage deep neural network for jointly estimating the pose of the goalie, their equipment, and the net. Experimental results using NHL benchmark data demonstrate that the proposed GoalieNet can achieve an average of 84\% accuracy across all keypoints, where 22 out of 29 keypoints are detected with more than 80\% accuracy. This indicates that such a joint pose estimation approach can be a promising research direction.

翻译：在计算机视觉驱动的冰球分析领域，守门员姿态估计是最具挑战性且研究最少的问题之一。与通用人体姿态估计不同，守门员姿态估计更为复杂，因为它不仅涉及检测隐藏在厚护垫和面罩下的守门员关节对应关键点，还需要检测大量非人体关键点，包括大型护腿板、手套、球杆以及球门。为应对这一挑战，我们提出了GoalieNet——一种用于联合估计守门员姿态、其装备及球门姿态的多阶段深度神经网络。使用NHL基准数据的实验结果表明，所提出的GoalieNet在所有关键点上平均达到84%的准确率，其中29个关键点中有22个的检测准确率超过80%。这表明这种联合姿态估计方法是一个具有前景的研究方向。