# Two Stream Network for Stroke Detection in Table Tennis

arXiv: https://arxiv.org/abs/2112.12073 · HTML: https://mlsanity.com/paper/2112.12073/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)

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

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022](https://mlsanity.com/paper/2301.13576/) (2023)

## Abstract (arXiv)

This paper presents a table tennis stroke detection method from videos. The method relies on a two-stream Convolutional Neural Network processing in parallel the RGB Stream and its computed optical flow. The method has been developed as part of the MediaEval 2021 benchmark for the Sport task. Our contribution did not outperform the provided baseline on the test set but has performed the best among the other participants with regard to the mAP metric.