PADELVIC: Multicamera videos and motion capture data of an amateur padel match

Abstract

Recent advances in computer vision and deep learning techniques have opened new possibilities regarding the automatic labeling of sport videos. However, an essential requirement for supervised techniques is the availability of accurately labeled training datasets. In this paper we present PadelVic, an annotated dataset of an amateur padel match which consists of multi-view video streams, accurate motion capture data of one of the players, as well as synthetic videos specifically designed to serve as training sets for convolutional neural networks estimating positional data from videos. As a demonstration of one of the applications of the dataset, we present a system for the accurate prediction of the center-of-mass of the players projected onto the court plane, from a single-view video of the match.