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

GoalieNet: A Multi-Stage Network for Joint Goalie, Equipment, and Net  Pose Estimation in Ice Hockey

Tracking and identifying players is a fundamental step in computer
vision-based ice hockey analytics. The data generated by tracking is used in
many other downstream tasks, such as game event detection and game strategy
analysis. Player tracking and identification is a challenging problem since the
motion of players in hockey is fast-paced and non-linear when compared to
pedestrians. There is also significant camera panning and zooming in hockey
broadcast video. Identifying players in ice hockey is challenging since the
players of the same team look almost identical, with the jersey number the only
discriminating factor between players. In this paper, an automated system to
track and identify players in broadcast NHL hockey videos is introduced. The
system is composed of three components (1) Player tracking, (2) Team
identification and (3) Player identification. Due to the absence of publicly
available datasets, the datasets used to train the three components are
annotated manually. Player tracking is performed with the help of a state of
the art tracking algorithm obtaining a Multi-Object Tracking Accuracy (MOTA)
score of 94.5%. For team identification, the away-team jerseys are grouped into
a single class and home-team jerseys are grouped in classes according to their
jersey color. A convolutional neural network is then trained on the team
identification dataset. The team identification network gets an accuracy of 97%
on the test set. A novel player identification model is introduced that
utilizes a temporal one-dimensional convolutional network to identify players
from player bounding box sequences. The player identification model further
takes advantage of the available NHL game roster data to obtain a player
identification accuracy of 83%.

该论文提出了一种自动系统来跟踪和识别广播 NHL 冰球录像中的球员，它包括三个组成部分：球员跟踪、队伍识别和球员识别。通过使用现有的追踪算法来跟踪球员，将远离队的队服分组为单一类别，主队的队服按照颜色分组，训练了一个卷积神经网络来识别团队。还引入了一种新颖的球员识别模型，利用一维卷积神经网络来识别球员的边界框序列，进一步利用 NHL 比赛花名册数据来获得 83％的球员识别准确率。