Multi-task learning for jersey number recognition in Ice Hockey

TL;DR

This paper introduces a multi-task learning network that simultaneously recognizes jersey numbers in ice hockey videos using holistic and digit-wise representations, improving accuracy over single-task models.

Contribution

The paper presents a novel multi-task learning approach combining holistic and digit-wise jersey number recognition, with optimized loss weighting for enhanced performance.

Findings

Multi-task network outperforms single-task models.

Optimal loss weights improve recognition accuracy.

Experimental results validate the effectiveness of the combined approach.

Abstract

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.