1. Approximating swimming trajectories with RBFs.
- Author
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De Santis, Giulia, Giulietti, Nicola, Caputo, Alessia, Castellini, Paolo, and Maponi, Pierluigi
- Subjects
- *
DEEP learning , *RADIAL basis functions , *POINT set theory , *SWIMMING , *VIDEO processing , *FISH locomotion - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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