Classification of Tennis Actions Using Deep Learning
Emil Hovad Therese Hougaard-Jensen Line Katrine Harder Clemmensen
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.