# Towards Automated Swimming Analytics Using Deep Neural Networks

arXiv: https://arxiv.org/abs/2001.04433 · HTML: https://mlsanity.com/paper/2001.04433/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Swimmer Stroke Rate Estimation From Overhead Race Video](https://mlsanity.com/paper/2104.12056/) (2021)
- [SwimXYZ: A large-scale dataset of synthetic swimming motions and videos](https://mlsanity.com/paper/2310.04360/) (2023)
- [Event Detection in Table Tennis Videos using 2D Keypoints](https://mlsanity.com/paper/2610.08286/) (2026)

## Abstract (arXiv)

Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper. There has not been much work on swimmer tracking or the creation of a swimmer database for machine learning purposes. Consequently, methods for collecting swimmer data from videos of swim competitions are explored and analyzed. The result is a guide to the creation of a comprehensive collection of swimming data suitable for training swimmer detection and tracking systems. With this database in place, systems can then be created to automate the collection of swimming analytics.