We present how to profitably approximate swimming trajectories leveraging Radial Basis Functions (RBFs). The data of these trajectories were obtained by recording athletes of the Deaf Olympic Italian National Team while swimming. In particular, collected videos were processed by U-NET, a deep learning model architecture, resulting in some sets of two-coordinates points of virtual targets. The obtained sets of points describe trajectories that are approximated with RBFs.
Approximating Swimming Trajectories with RBFs
Maponi P.
2024-01-01
Abstract
We present how to profitably approximate swimming trajectories leveraging Radial Basis Functions (RBFs). The data of these trajectories were obtained by recording athletes of the Deaf Olympic Italian National Team while swimming. In particular, collected videos were processed by U-NET, a deep learning model architecture, resulting in some sets of two-coordinates points of virtual targets. The obtained sets of points describe trajectories that are approximated with RBFs.File in questo prodotto:
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