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.

本研究提出了一个名为GoalieNet的多阶段深度神经网络，可用于联合估计门将，他们的装备和球网的关键点检测，实验结果表明所提出的GoalieNet可以在所有关键点上实现84％的平均准确性，其中29个关键点中的22个关键点检测准确率高达80％以上。

GoalieNet：一种用于冰球守门员、装备和球门联合姿态估计的多阶段网络