Analyzing swimming performance using drone captured aerial videos

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...

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Main Authors: TRAN, Ngoc Doan Thu, CHOO, Kenny Tsu Wei, FOONG, Shaohui, BHARDWAJ, Hitesh, WIN, Shane Kyi Hla, ANG, Wei Jun, GOH, Kenneth T., BALAN, Rajesh Krishna
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
UAV
Online Access:https://ink.library.smu.edu.sg/sis_research/9848
https://ink.library.smu.edu.sg/context/sis_research/article/10848/viewcontent/3661810.3663464.pdf
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spelling sg-smu-ink.sis_research-108482024-12-24T03:24:02Z Analyzing swimming performance using drone captured aerial videos TRAN, Ngoc Doan Thu CHOO, Kenny Tsu Wei FOONG, Shaohui BHARDWAJ, Hitesh WIN, Shane Kyi Hla ANG, Wei Jun GOH, Kenneth T. BALAN, Rajesh Krishna 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. 2024-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9848 info:doi/10.1145/3661810.36634 https://ink.library.smu.edu.sg/context/sis_research/article/10848/viewcontent/3661810.3663464.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Swimming movement monitoring UAV Pose detection Computer vision Tracking systems Artificial Intelligence and Robotics Operations Research, Systems Engineering and Industrial Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Swimming movement monitoring
UAV
Pose detection
Computer vision
Tracking systems
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
spellingShingle Swimming movement monitoring
UAV
Pose detection
Computer vision
Tracking systems
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
TRAN, Ngoc Doan Thu
CHOO, Kenny Tsu Wei
FOONG, Shaohui
BHARDWAJ, Hitesh
WIN, Shane Kyi Hla
ANG, Wei Jun
GOH, Kenneth T.
BALAN, Rajesh Krishna
Analyzing swimming performance using drone captured aerial videos
description 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.
format text
author TRAN, Ngoc Doan Thu
CHOO, Kenny Tsu Wei
FOONG, Shaohui
BHARDWAJ, Hitesh
WIN, Shane Kyi Hla
ANG, Wei Jun
GOH, Kenneth T.
BALAN, Rajesh Krishna
author_facet TRAN, Ngoc Doan Thu
CHOO, Kenny Tsu Wei
FOONG, Shaohui
BHARDWAJ, Hitesh
WIN, Shane Kyi Hla
ANG, Wei Jun
GOH, Kenneth T.
BALAN, Rajesh Krishna
author_sort TRAN, Ngoc Doan Thu
title Analyzing swimming performance using drone captured aerial videos
title_short Analyzing swimming performance using drone captured aerial videos
title_full Analyzing swimming performance using drone captured aerial videos
title_fullStr Analyzing swimming performance using drone captured aerial videos
title_full_unstemmed Analyzing swimming performance using drone captured aerial videos
title_sort analyzing swimming performance using drone captured aerial videos
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9848
https://ink.library.smu.edu.sg/context/sis_research/article/10848/viewcontent/3661810.3663464.pdf
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