Semi-Supervised Training to Improve Player and Ball Detection in Soccer

TL;DR

This paper introduces a semi-supervised learning approach using a teacher-student model to enhance player and ball detection in soccer videos, reducing the need for extensive labeled data and improving detection accuracy.

Contribution

A novel semi-supervised training method leveraging unlabeled soccer videos with a teacher-student architecture and confidence-based loss parametrizations.

Findings

Significant performance improvements with unlabeled data inclusion

First benchmark on SoccerNet-v3 detection task with 52.3% mAP

Effective training loss parametrizations for teacher confidence

Abstract

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%. Our code is available at https://github.com/rvandeghen/SST .