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

Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal, Jenny Benois-Pineau, Renaud Péteri +2 more
1/31/2023
cs.AIcs.CVcs.HC

No official code found. Best implementation: ccp-eva/SportTaskME22 (6 stars, last commit 2024-05-14).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

ccp-eva/SportTaskME22Community100%
61PythonApr 28, 20222 years ago

Similar Papers

Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data

85% match2023

Extended OpenTT Games Dataset: A table tennis dataset for fine-grained shot type and point outcome

85% match2025

BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports

83% match1 repo522025

ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis

83% match2024

Classification of Tennis Actions Using Deep Learning

83% match2024

Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks

81% match2023

Cite this paper

@article{martin2023sport,
  title  = {Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022},
  author = {Pierre-Etienne Martin and Jordan Calandre and Boris Mansencal and Jenny Benois-Pineau and Renaud Péteri and Laurent Mascarilla and Julien Morlier},
  year   = {2023},
  eprint = {2301.13576},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.13576v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2301.13576.svg)](https://codeofpaper.com/paper/2301.13576)

Discussion