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...
Saved in:
Main Authors: | , , , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-10848 |
---|---|
record_format |
dspace |
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 |
_version_ |
1820027798667919360 |