mlsanityData to 13 Oct 2026

Swimmer Stroke Rate Estimation From Overhead Race Video

arXiv 2104.12056 ↗First posted 25 Apr 2021eess.IV cs.CVcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 25 Apr 2021.

Builds on

8 of 24 references.

References · 8

  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,128
  2. 2018YOLOv3: An Incremental Improvementcited by 2,655
  3. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  4. 2016Simple Online and Realtime Trackingcited by 864
  5. 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 687
  6. 2015MOTChallenge 2015: Towards a Benchmark for Multi-Target Trackingcited by 133
  7. 2020Towards Automated Swimming Analytics Using Deep Neural Networkscited by 3
  8. 2017Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networkscited by 2

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Citing papers per year: 2022: 2.

2 papers.