# Swimmer Stroke Rate Estimation From Overhead Race Video

arXiv: https://arxiv.org/abs/2104.12056 · HTML: https://mlsanity.com/paper/2104.12056/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)
- [Towards Automated Swimming Analytics Using Deep Neural Networks](https://mlsanity.com/paper/2001.04433/) (2020)
- [Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1705.09894/) (2017)

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

- [Recognition of Freely Selected Keypoints on Human Limbs](https://mlsanity.com/paper/2204.06326/) (2022)
- [Towards Automated Key-Point Detection in Images with Partial Pool View](https://mlsanity.com/paper/2208.05641/) (2022)

## Abstract (arXiv)

In this work, we propose a swimming analytics system for automatically determining swimmer stroke rates from overhead race video (ORV). General ORV is defined as any footage of swimmers in competition, taken for the purposes of viewing or analysis. Examples of this are footage from live streams, broadcasts, or specialized camera equipment, with or without camera motion. These are the most typical forms of swimming competition footage. We detail how to create a system that will automatically collect swimmer stroke rates in any competition, given the video of the competition of interest. With this information, better systems can be created and additions to our analytics system can be proposed to automatically extract other swimming metrics of interest.