# Player Identification in Hockey Broadcast Videos

arXiv: https://arxiv.org/abs/2009.02429 · HTML: https://mlsanity.com/paper/2009.02429/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Efficient tracking of team sport players with few game-specific annotations](https://mlsanity.com/paper/2204.04049/) (2022)
- [Ice hockey player identification via transformers and weakly supervised learning](https://mlsanity.com/paper/2111.11535/) (2021)
- [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)
- [PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics](https://mlsanity.com/paper/2405.07407/) (2024)
- [Domain-Guided Masked Autoencoders for Unique Player Identification](https://mlsanity.com/paper/2403.11328/) (2024)

## Abstract (arXiv)

We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spatio-temporal features of jersey numbers over time and learn long-term dependencies. For this work, we created a new hockey player tracklet dataset that contains sequences of hockey player bounding boxes. Additionally, we employ a secondary 1-dimensional convolutional neural network classifier as a late score-level fusion method to classify the output of the ResNet+LSTM network. This achieves an overall player identification accuracy score over 87% on the test split of our new dataset.