Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3661810.3663464acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article
Open access

Analyzing Swimming Performance Using Drone Captured Aerial Videos

Published: 04 June 2024 Publication History

Abstract

Monitoring swimmer performance is crucial for improving training and enhancing athletic techniques. Traditional methods for tracking swimmers, such as above-water and underwater cameras, face limitations due to the need for multiple cameras and obstructions from water splashes. This paper presents a novel approach for tracking swimmers using a moving UAV. The proposed system employs a UAV equipped with a high-resolution camera to capture aerial footage of the swimmers. The footage is then processed using computer vision algorithms to extract the swimmers' positions and movements. This approach offers several advantages, including single camera use and comprehensive coverage. The system's accuracy is evaluated with both training and in competition videos. The results demonstrate the system's ability to accurately track swimmers' movements, limb angles, stroke duration and velocity with the maximum error of 0.3 seconds and 0.35 m/s for stroke duration and velocity, respectively.

References

[1]
Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, and Matthias Grundmann. 2020. Blazepose: On-device real-time body pose tracking. arXiv preprint arXiv:2006.10204 (2020).
[2]
Xiaowen Cao and Wei Qi Yan. 2023. Pose estimation for swimmers in video surveillance. Multimedia Tools and Applications (2023), 1--16.
[3]
Diogo D Carvalho, Susana Soares, Rodrigo Zacca, Daniel A Marinho, António J Silva, David B Pyne, J Paulo Vilas-Boas, and Ricardo J Fernandes. 2019. In-water and on-land swimmers' symmetry and force production. International Journal of Environmental Research and Public Health 16, 24 (2019), 5018.
[4]
DJI. 2024. Mavic 3 Pro. (2024). https://www.dji.com/sg/mavic-3-pro
[5]
Hossein Fani, Amin Mirlohi, Hawre Hosseini, and Rainer Herperst. 2018. Swim stroke analytic: Front crawl pulling pose classification. In 2018 25th IEEE international conference on image processing (ICIP). IEEE, 4068--4072.
[6]
Giorgio Gatta, Matteo Cortesi, Francesco Lucertini, Piero Benelli, Davide Sisti, and Silvia Fantozzi. 2015. Path linearity of elite swimmers in a 400 m front crawl competition. Journal of Sports Science & Medicine 14, 1 (2015), 69.
[7]
Nicola Giulietti, Alessia Caputo, Paolo Chiariotti, and Paolo Castellini. 2023. Swimmernet: Underwater 2d swimmer pose estimation exploiting fully convolutional neural networks. Sensors 23, 4 (2023), 2364.
[8]
Google. 2024. Pose landmark detection guide. (2024). https://developers.google.com/mediapipe/solutions/vision/pose_landmarker
[9]
R Havriluk. 2007. Analyzing hand force in swimming: bilateral symmetry. American Swimming Magazine 1 (2007), 34--38.
[10]
Marcin Jaszczak. 2008. The dynamical asymmetry of the upper extremities during symmetrical exercises. Human movement 9, 2 (2008), 116--120.
[11]
RO Robinson, Walter Herzog, and Benno M Nigg. 1987. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. Journal of manipulative and physiological therapeutics 10, 4 (1987), 172--176.
[12]
Ross H Sanders, Malcolm M Fairweather, Alison Alcock, and Carla B McCabe. 2015. An approach to identifying the effect of technique asymmetries on body alignment in swimming exemplified by a case study of a breaststroke swimmer. Journal of sports science & medicine 14, 2 (2015), 304.
[13]
Karini B Santos, Paulo CB Bento, Carl Payton, and André LF Rodacki. 2020. Symmetry in the front crawl stroke of different skill level of able-bodied and disabled swimmers. Plos one 15, 3 (2020), e0229918.
[14]
Kohei Toyoda, Michinari Kono, and Jun Rekimoto. 2019. Shooting swimmers using aerial and underwater drone for 3D pose estimation. In 1st International Workshop on Human-Drone Interaction.
[15]
Yu Ukai and Jun Rekimoto. 2013. Swimoid: a swim support system using an underwater buddy robot. In Proceedings of the 4th Augmented Human International Conference. 170-177.
[16]
Santiago Veiga, Antonio Cala, Pablo González Frutos, and Enrique Navarro. 2013. Kinematical comparison of the 200 m backstroke turns between national and regional level swimmers. Journal of sports science & medicine 12, 4 (2013), 730.
[17]
Dan Zecha, Moritz Einfalt, Christian Eggert, and Rainer Lienhart. 2018. Kinematic pose rectification for performance analysis and retrieval in sports. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1791-1799.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DroNet '24: Proceedings of the 10th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications
June 2024
57 pages
ISBN:9798400706561
DOI:10.1145/3661810
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2024

Check for updates

Author Tags

  1. UAV
  2. computer vision
  3. pose detection
  4. sport
  5. swimming

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

MOBISYS '24
Sponsor:

Acceptance Rates

Overall Acceptance Rate 29 of 50 submissions, 58%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 90
    Total Downloads
  • Downloads (Last 12 months)90
  • Downloads (Last 6 weeks)39
Reflects downloads up to 18 Aug 2024

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media