Siamese Spatio-temporal convolutional neural network for stroke classification in Table Tennis games - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Siamese Spatio-temporal convolutional neural network for stroke classification in Table Tennis games

Abstract

This work presents a Table Tennis stroke classification approach through a siamese spatio-temporal convolutional neural network-SSTCNN. The videos are recorded at 120 frames per second with players performing in natural conditions. The frames are extracted, resized and processed to compute the optical flow. From the optical flow, a region of interest-ROI-is inferred. The SSTCNN is then feed by RGB and optical flow ROIs stream to give a probabilistic classification over all the table tennis strokes.
Fichier principal
Vignette du fichier
MediaEval_19_paper_58.pdf (1.76 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02937668 , version 1 (14-09-2020)

Identifiers

  • HAL Id : hal-02937668 , version 1

Cite

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Julien Morlier. Siamese Spatio-temporal convolutional neural network for stroke classification in Table Tennis games. MediaEval 2019 Workshop, Oct 2019, Sophia Antipolis, France. ⟨hal-02937668⟩

Collections

CNRS
169 View
46 Download

Share

Gmail Facebook X LinkedIn More