Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020

Pierre-Etienne Martin (1, 2) , Jenny Benois-Pineau (1, 2) , Boris Mansencal (1, 3) , Renaud Péteri , Julien Morlier

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 UB - Université de Bordeaux
3 Bordeaux INP - Institut Polytechnique de Bordeaux

This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. The finegrained classification is performed on trimmed video segments recorded at 120 fps with different players performing in natural conditions. From those segments, the frames are extracted, their optical flow is computed and the pose of the player is estimated. From the optical flow amplitude, a region of interest is inferred. A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification.

Domaines: Réseau de neurones [cs.NE]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Multimédia [cs.MM]; Apprentissage [cs.LG]; Interface homme-machine [cs.HC]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Julien Morlier. Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. MediaEval 2020 Workshop, Dec 2020, Online, Unknown Region. ⟨hal-03104275⟩