10 research outputs found
Player tracking and identification in broadcast ice hockey video
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 player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Identifying players in ice hockey is a difficult task since the players of the same team appear almost identical, with the jersey number the only consistent discriminating factor between players.
In this thesis, an automated system to track and identify players in broadcast NHL hockey videos is introduced. The system is composed of player tracking, team identification and player identification models. In addition, the game roster and player shift data is incorporated to further increase the accuracy of player identification in the overall system. Due to the absence of publicly available datasets, new datasets for player tracking, team identification and player identification in ice-hockey are also introduced.
Remarking that there is a lack of publicly available research for tracking ice hockey players making use of recent advancements in deep learning, we test five state-of-the-art tracking algorithms on an ice-hockey dataset and analyze the performance and failure cases.
We introduce a multi-task loss based network to identify player jersey numbers from static images. The network uses multi-task learning to simultaneously predict and learn from two different representations of a player jersey number. Through various experiments and ablation studies it was demonstrated that the multi-task learning based network performed better than the constituent single-task settings.
We incorporate the temporal dimension into account for jersey number identification by inferring jersey number from sequences of player images - called player tracklets. To do so, we tested two popular deep temporal networks (1) Temporal 1D convolutional neural network (CNN) and (2) Transformer network. The network trained using the multi-task loss served as a backbone for these two networks. In addition, we also introduce a weakly-supervised learning strategy to improve training speed and convergence for the transformer network. Experimental results demonstrate that the proposed networks outperform the state-of-the art.
Finally, we describe in detail how the player tracking and identification models are put together to form the holistic pipeline starting from raw broadcast NHL video to obtain uniquely identified player tracklets. The process of incorporating the game roster and player shifts to improve player identification is explained. An overall accuracy of 88% is obtained on the test set. An off-the-shelf automatic homography registration model and a puck localization model are also incorporated into the pipeline to obtain the tracks of both player and puck on the ice rink
Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation
In keypoint estimation tasks such as human pose estimation, heatmap-based
regression is the dominant approach despite possessing notable drawbacks:
heatmaps intrinsically suffer from quantization error and require excessive
computation to generate and post-process. Motivated to find a more efficient
solution, we propose to model individual keypoints and sets of spatially
related keypoints (i.e., poses) as objects within a dense single-stage
anchor-based detection framework. Hence, we call our method KAPAO (pronounced
"Ka-Pow"), for Keypoints And Poses As Objects. KAPAO is applied to the problem
of single-stage multi-person human pose estimation by simultaneously detecting
human pose and keypoint objects and fusing the detections to exploit the
strengths of both object representations. In experiments, we observe that KAPAO
is faster and more accurate than previous methods, which suffer greatly from
heatmap post-processing. The accuracy-speed trade-off is especially favourable
in the practical setting when not using test-time augmentation. Source code:
https://github.com/wmcnally/kapao
Evaluating deep tracking models for player tracking in broadcast ice hockey video
Tracking and identifying players is an important problem in computer vision
based ice hockey analytics. Player tracking is a challenging problem since the
motion of players in hockey is fast-paced and non-linear. There is also
significant player-player and player-board occlusion, camera panning and
zooming in hockey broadcast video. Prior published research perform player
tracking with the help of handcrafted features for player detection and
re-identification. Although commercial solutions for hockey player tracking
exist, to the best of our knowledge, no network architectures used, training
data or performance metrics are publicly reported. There is currently no
published work for hockey player tracking making use of the recent advancements
in deep learning while also reporting the current accuracy metrics used in
literature. Therefore, in this paper, we compare and contrast several
state-of-the-art tracking algorithms and analyze their performance and failure
modes in ice hockey.Comment: Accepted to Link\"oping Hockey Analytics Conference (LINHAC). arXiv
admin note: substantial text overlap with arXiv:2110.0309