Recent advances of deep learning makes it possible to identify specific
events in videos with greater precision. This has great relevance in sports
like tennis in order to e.g., automatically collect game statistics, or replay
actions of specific interest for game strategy or player improvements. In this
paper, we investigate the potential and the challenges of using deep learning
to classify tennis actions. Three models of different size, all based on the
deep learning architecture SlowFast were trained and evaluated on the academic
tennis dataset THETIS. The best models achieve a generalization accuracy of 74
%, demonstrating a good performance for tennis action classification. We
provide an error analysis for the best model and pinpoint directions for
improvement of tennis datasets in general. We discuss the limitations of the
data set, general limitations of current publicly available tennis data-sets,
and future steps needed to make progress.

通过对深度学习在网球行为分类中的应用进行研究，本文探讨了其潜力和挑战。我们使用三种不同规模的基于深度学习架构 SlowFast 的模型对学术网球数据集 THETIS 进行训练和评估。最佳模型的泛化准确率达到了 74%，为网球行为分类的良好性能提供了证明。我们提供了最佳模型的错误分析，并指出了改进网球数据集的方向，讨论了数据集的限制、当前公开网球数据集的一般限制以及未来的进展方向。