Video-based Skill Assessment for Golf: Estimating Golf Handicap

Christian Keilstrup Ingwersen; Artur Xarles Esparraguera; Albert Clapés; Meysam Madadi; Janus Nørtoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera

doi:10.1145/3606038.3616150

Video-based Skill Assessment for Golf: Estimating Golf Handicap

Christian Keilstrup Ingwersen, Artur Xarles Esparraguera, Albert Clapés, Meysam Madadi, Janus Nørtoft Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Sergio Escalera

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf

Original language	English
Title of host publication	Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports
Publisher	Association for Computing Machinery
Publication date	2023
Pages	31-39
ISBN (Electronic)	979-8-4007-0269-3
DOIs	https://doi.org/10.1145/3606038.3616150
Publication status	Published - 2023
Event	6th International Workshop on Multimedia Content Analysis in Sports - Ottawa, Canada Duration: 29 Oct 2023 → 29 Oct 2023 Conference number: 6

Conference

Conference	6th International Workshop on Multimedia Content Analysis in Sports
Number	6
Country/Territory	Canada
City	Ottawa
Period	29/10/2023 → 29/10/2023

Keywords

Datasets
Neural networks
Action quality assessment
Golf
Action understanding

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10.1145/3606038.3616150

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Ingwersen, C. K., Esparraguera, A. X., Clapés, A., Madadi, M., Jensen, J. N., Hannemose, M. R., Dahl, A. B., & Escalera, S. (2023). Video-based Skill Assessment for Golf: Estimating Golf Handicap. In Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports (pp. 31-39). Association for Computing Machinery. https://doi.org/10.1145/3606038.3616150

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title = "Video-based Skill Assessment for Golf: Estimating Golf Handicap",

abstract = "Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf",

keywords = "Datasets, Neural networks, Action quality assessment, Golf, Action understanding",

author = "Ingwersen, {Christian Keilstrup} and Esparraguera, {Artur Xarles} and Albert Clap{\'e}s and Meysam Madadi and Jensen, {Janus N{\o}rtoft} and Hannemose, {Morten Rieger} and Dahl, {Anders Bjorholm} and Sergio Escalera",

year = "2023",

doi = "10.1145/3606038.3616150",

language = "English",

pages = "31--39",

booktitle = "Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports",

publisher = "Association for Computing Machinery",

address = "United States",

note = "6th International Workshop on Multimedia Content Analysis in Sports, MMSports ; Conference date: 29-10-2023 Through 29-10-2023",

}

Ingwersen, CK, Esparraguera, AX, Clapés, A, Madadi, M, Jensen, JN, Hannemose, MR , Dahl, AB & Escalera, S 2023, Video-based Skill Assessment for Golf: Estimating Golf Handicap. in Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, pp. 31-39, 6th International Workshop on Multimedia Content Analysis in Sports, Ottawa, Ontario, Canada, 29/10/2023. https://doi.org/10.1145/3606038.3616150

Video-based Skill Assessment for Golf: Estimating Golf Handicap. / Ingwersen, Christian Keilstrup; Esparraguera, Artur Xarles; Clapés, Albert et al.
Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, 2023. p. 31-39.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Video-based Skill Assessment for Golf

T2 - 6th International Workshop on Multimedia Content Analysis in Sports

AU - Ingwersen, Christian Keilstrup

AU - Esparraguera, Artur Xarles

AU - Clapés, Albert

AU - Madadi, Meysam

AU - Jensen, Janus Nørtoft

AU - Hannemose, Morten Rieger

AU - Dahl, Anders Bjorholm

AU - Escalera, Sergio

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PY - 2023

Y1 - 2023

N2 - Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf

AB - Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf

KW - Datasets

KW - Neural networks

KW - Action quality assessment

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KW - Action understanding

U2 - 10.1145/3606038.3616150

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M3 - Article in proceedings

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BT - Proceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports

PB - Association for Computing Machinery

Y2 - 29 October 2023 through 29 October 2023

ER -

Video-based Skill Assessment for Golf: Estimating Golf Handicap

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