A General Framework for Jersey Number Recognition in Sports Video

Maria Koshkina, James H. Elder; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3235-3244

Jersey number recognition is an important task in sports video analysis partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code models and data are available at https://github.com/mkoshkina/jerseynumber-pipeline.