# Ice hockey player identification via transformers and weakly supervised learning

arXiv: https://arxiv.org/abs/2111.11535 · HTML: https://mlsanity.com/paper/2111.11535/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [Video Action Transformer Network](https://mlsanity.com/paper/1812.02707/) (2018)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Pose Recognition with Cascade Transformers](https://mlsanity.com/paper/2104.06976/) (2021)
- [Actor-Transformers for Group Activity Recognition](https://mlsanity.com/paper/2003.12737/) (2020)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [GolfDB: A Video Database for Golf Swing Sequencing](https://mlsanity.com/paper/1903.06528/) (2019)
- [Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions](https://mlsanity.com/paper/2004.06172/) (2020)
- [Automated Top View Registration of Broadcast Football Videos](https://mlsanity.com/paper/1703.01437/) (2017)
- [Multi-task learning for jersey number recognition in Ice Hockey](https://mlsanity.com/paper/2108.07848/) (2021)
- [Temporal Hockey Action Recognition via Pose and Optical Flows](https://mlsanity.com/paper/1812.09533/) (2018)
- [Player Identification in Hockey Broadcast Videos](https://mlsanity.com/paper/2009.02429/) (2020)

## Built on by (most cited first, 14 of 14)

- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Towards Active Learning for Action Spotting in Association Football Videos](https://mlsanity.com/paper/2304.04220/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [A General Framework for Jersey Number Recognition in Sports Video](https://mlsanity.com/paper/2405.13896/) (2024)
- [Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos](https://mlsanity.com/paper/2309.06285/) (2023)
- [All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes](https://mlsanity.com/paper/2304.02939/) (2023)
- [CLIP-ReIdent: Contrastive Training for Player Re-Identification](https://mlsanity.com/paper/2303.11855/) (2023)
- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics](https://mlsanity.com/paper/2405.07407/) (2024)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Domain-Guided Masked Autoencoders for Unique Player Identification](https://mlsanity.com/paper/2403.11328/) (2024)
- [Runner re-identification from single-view running video in the open-world setting](https://mlsanity.com/paper/2310.11700/) (2023)
- [Generalized Jersey Number Recognition Using Multi-task Learning With Orientation-guided Weight Refinement](https://mlsanity.com/paper/2406.01033/) (2024)

## Abstract (arXiv)

Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in Natural Language Processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, in this paper, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (also called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the dataset used. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player shifts improved the player identification accuracy by 6%.