% pubman genre = conference-paper
@inproceedings{item_3376888,
title = {{Sports video: Fine-grained action detection and classification of table tennis strokes from videos for MediaEval 2021}},
author = {Martin, Pierre-Etienne and Calandre, Jordan and Mansencal, Boris and Benois-Pineau, Jenny and Mascarilla, Renaud P{\'e}teri Laurent and Morlier, Julien},
language = {eng},
url = {https://2021.multimediaeval.com/},
year = {2021},
abstract = {{This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task.}},
booktitle = {{Proceedings MediaEval 2021 : Multimedia Benchmark Workshop}},
address = {Online},
note = {MediaEval 2021 Workshop},
}