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dc.contributor.authorJavadiha, Mohammadreza
dc.contributor.authorAndujar, Carlos
dc.contributor.authorLacasa Claver, Enric
dc.contributor.authorRic Diez, Ángel
dc.contributor.authorSusin, Antonio
dc.description.abstractThe estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.ca_ES
dc.description.sponsorshipThis work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under grant TIN2017-88515-C2-1-R.ca_ES
dc.relation.isformatofReproducció del document publicat a
dc.relation.ispartofSensors, 2021, vol. 21, núm. 10, 3368ca_ES
dc.rightscc-by (c) Javadiha, et al., 2021ca_ES
dc.subjectSports scienceca_ES
dc.subjectRacket sportsca_ES
dc.subjectDeep learningca_ES
dc.subjectPose estimationca_ES
dc.subjectPlayer trackingca_ES
dc.subjectTracking dataca_ES
dc.titleEstimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methodsca_ES

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