# Multi-task learning for jersey number recognition in Ice Hockey

arXiv: https://arxiv.org/abs/2108.07848 · HTML: https://mlsanity.com/paper/2108.07848/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1312.6082/) (2013)

## Built on by (most cited first, 15 of 15)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [Ice hockey player identification via transformers and weakly supervised learning](https://mlsanity.com/paper/2111.11535/) (2021)
- [Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking](https://mlsanity.com/paper/2401.09942/) (2024)
- [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)
- [Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data](https://mlsanity.com/paper/2203.00734/) (2022)
- [Automated player identification and indexing using two-stage deep learning network](https://mlsanity.com/paper/2204.13809/) (2022)
- [Domain-Guided Masked Autoencoders for Unique Player Identification](https://mlsanity.com/paper/2403.11328/) (2024)
- [Towards long-term player tracking with graph hierarchies and domain-specific features](https://mlsanity.com/paper/2502.21242/) (2025)
- [From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction](https://mlsanity.com/paper/2504.06357/) (2025)
- [Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset](https://mlsanity.com/paper/2311.09256/) (2023)
- [Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing](https://mlsanity.com/paper/2402.08025/) (2024)
- [Generalized Jersey Number Recognition Using Multi-task Learning With Orientation-guided Weight Refinement](https://mlsanity.com/paper/2406.01033/) (2024)
- [Ice Hockey Puck Localization Using Contextual Cues](https://mlsanity.com/paper/2506.04365/) (2025)

## Abstract (arXiv)

Identifying players in sports videos by recognizing their jersey numbers is a challenging task in computer vision. We have designed and implemented a multi-task learning network for jersey number recognition. In order to train a network to recognize jersey numbers, two output label representations are used (1) Holistic - considers the entire jersey number as one class, and (2) Digit-wise - considers the two digits in a jersey number as two separate classes. The proposed network learns both holistic and digit-wise representations through a multi-task loss function. We determine the optimal weights to be assigned to holistic and digit-wise losses through an ablation study. Experimental results demonstrate that the proposed multi-task learning network performs better than the constituent holistic and digit-wise single-task learning networks.