Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

View/ Open
Issue date
2021Suggested citation
Javadiha, Mohammadreza;
Andujar, Carlos;
Lacasa Claver, Enrique;
Ric Diez, Ángel;
Susin, Antonio;
.
(2021)
.
Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods.
Sensors, 2021, vol. 21, núm. 10, 3368.
https://doi.org/10.3390/s21103368.
Metadata
Show full item recordAbstract
The 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.
Is part of
Sensors, 2021, vol. 21, núm. 10, 3368European research projects
Collections
The following license files are associated with this item: