  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.
