GoalieNet: A Multi-Stage Network for Joint Goalie, Equipment, and Net Pose Estimation in Ice Hockey
This addresses a specific problem for ice hockey analytics by providing a method for joint pose estimation, but it is incremental as it builds on existing human pose estimation techniques for a niche domain.
The paper tackled the challenging task of goalie pose estimation in ice hockey, which involves detecting keypoints for the goalie, equipment, and net, and achieved an average accuracy of 84% across all keypoints, with 22 out of 29 keypoints detected at over 80% accuracy.
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.