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

arXiv: https://arxiv.org/abs/2301.13576 · HTML: https://mlsanity.com/paper/2301.13576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [A Short Note on the Kinetics-700-2020 Human Action Dataset](https://mlsanity.com/paper/2010.10864/) (2020)
- [Long-term Temporal Convolutions for Action Recognition](https://mlsanity.com/paper/1604.04494/) (2016)
- [The AVA-Kinetics Localized Human Actions Video Dataset](https://mlsanity.com/paper/2005.00214/) (2020)
- [Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark](https://mlsanity.com/paper/2112.12074/) (2021)
- [Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021](https://mlsanity.com/paper/2112.11384/) (2021)
- [Two Stream Network for Stroke Detection in Table Tennis](https://mlsanity.com/paper/2112.12073/) (2021)
- [3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition](https://mlsanity.com/paper/2204.08460/) (2022)

## Built on by (most cited first, 2 of 2)

- [Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms](https://mlsanity.com/paper/2302.02752/) (2023)
- [Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks](https://mlsanity.com/paper/2302.02755/) (2023)

## Abstract (arXiv)

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.