Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022

Pierre-Etienne Martin (1) , Jordan Calandre (2) , Boris Mansencal (3) , Jenny Benois-Pineau (3) , Renaud Péteri (2) , Laurent Mascarilla (2) , Julien Morlier (4)

1 MPI-EVA - Max-Planck-Institut für evolutionäre Anthropologie
2 MIA - Mathématiques, Image et Applications - EA 3165
3 LaBRI - Laboratoire Bordelais de Recherche en Informatique
4 IMS Laboratory- Bordeaux University

Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this task aims at detecting and classifying subtle movements from sport videos. We focus on recordings of table tennis matches. Conducted since 2019, this task provides a classification challenge from untrimmed videos recorded under natural conditions with known temporal boundaries for each stroke. Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos. This year, the training, validation, and test sets are enhanced to ensure that all strokes are represented in each dataset. The dataset is now similar to the one used in [1, 2]. This research is intended to build tools for coaches and athletes who want to further evaluate their sport performances.

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

Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal, Jenny Benois-Pineau, Renaud Péteri, et al.. Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022. MediaEval 2022 Workshop, Jan 2023, Bergen, Norway. ⟨hal-03958838⟩