  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 .
