论文标题
半监督训练以改善足球比赛的球员和球检测
Semi-Supervised Training to Improve Player and Ball Detection in Soccer
论文作者
论文摘要
近年来，对于体育分析，准确的球员和球检测变得越来越重要。由于大多数最先进的方法都依赖于以有监督的方式培训深度学习网络，因此它们需要大量的带注释的数据，这些数据很少可用。在本文中，我们提出了一种新颖的通用半监督方法，通过利用大型无标记的足球广播视频数据集来培训基于标记的图像数据集的网络。更确切地说，我们设计了一种教师研究方法，其中教师对未标记的数据进行替代注释，以后将用于培训与老师相同的架构的学生。此外，我们介绍了三个培训损失参数，使学生可以根据提案置信度得分来怀疑教师在培训期间的预测。我们表明，在培训过程中包括未标记的数据，可以实质上改善仅在标记数据上训练的检测网络的性能。最后，我们提供了一项详尽的性能研究，包括不同比例的标记和未标记数据，并在新的Soccernet-V3检测任务上建立了第一个基准，地图为52.3％。我们的代码可在https://github.com/rvandeghen/sst上找到。
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 .
