Two Weakly Supervised Approaches for Role Classification of Soccer Players

Role classification of players according to their uniform or playing kit in unseen soccer game scenes remains a challenging problem. While multiple methods have being proposed for this task, both handcrafted and deep learning methods have been designed to work on individual games only. Moreover, several of them require costly annotations, constrain the problem to two categories, and/or work on a multiple camera setting. We propose two weakly supervised approaches for role classification of soccer players that address these problems and classify them in five categories, namely, referee, outfield players from team 1 and team 2, and goalkeeper A and goalkeeper B, that work for any (unseen) game. Both approaches learn a robust representation of playing kits using metric learning with weakly annotated data. Our first approach, called embedding clustering, calculates player kit embeddings in an unseen game and clusters them also leveraging the field locations of the players. Our second approach, TransKit, is a transformer architecture designed to generalize across multiple unseen games from a small set of training games. TransKit is trained on different possible games using combinatorial data augmentation. Our methods obtain an accuracy of 97.45% and 90.45% in our annotated benchmark of unseen games with seen uniforms during training, respectively.

@inproceedings{cartas2024PlayerClassification,
author = {Cartas, Alejandro and Ballester, Coloma and Haro, Gloria},
title = {Two Weakly Supervised Approaches for Role Classification of Soccer Players},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 7th International ACM Workshop on Multimedia Content Analysis in Sports},
series = {MMSports '24}
location = {Melbourne, Australia},
year = {2024},
}