Application of computer vision and vector space model for tactical movement classification in badminton

Kokum Weeratunga; Anuja Dharmaratne; Khoo Boon How

doi:10.1109/CVPRW.2017.22

Application of computer vision and vector space model for tactical movement classification in badminton

Kokum Weeratunga, Anuja Dharmaratne, Khoo Boon How

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

16 Citations (Scopus)

Abstract

Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.

Original language	English
Title of host publication	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Editors	Yanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	132-138
Number of pages	7
ISBN (Electronic)	9781538607336
ISBN (Print)	9781538607343
DOIs	https://doi.org/10.1109/CVPRW.2017.22
Publication status	Published - 2017
Event	IEEE International Workshop on Computer Vision in Sports 2017 - Honolulu, United States of America Duration: 21 Jul 2017 → 26 Jul 2017 Conference number: 3rd https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/8014302/proceeding (Proceedings) https://vap.aau.dk/cvsports/3rd-ieee-international-workshop-in-computer-vision-in-sports-at-cvpr-2017/ (Website)

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher	The Institute of Electrical and Electronics Engineers, Inc.
Volume	2017-July
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	IEEE International Workshop on Computer Vision in Sports 2017
Country/Territory	United States of America
City	Honolulu
Period	21/07/17 → 26/07/17
Internet address	https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/8014302/proceeding (Proceedings) https://vap.aau.dk/cvsports/3rd-ieee-international-workshop-in-computer-vision-in-sports-at-cvpr-2017/ (Website)

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10.1109/CVPRW.2017.22

Cite this

Weeratunga, K., Dharmaratne, A., & How, K. B. (2017). Application of computer vision and vector space model for tactical movement classification in badminton. In Y. Liu, J. M. Rehg, C. J. Taylor, & Y. Wu (Eds.), Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 (pp. 132-138). (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2017-July). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPRW.2017.22

Weeratunga, Kokum ; Dharmaratne, Anuja ; How, Khoo Boon. / Application of computer vision and vector space model for tactical movement classification in badminton. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. editor / Yanxi Liu ; James M. Rehg ; Camillo J. Taylor ; Ying Wu. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 132-138 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

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abstract = "Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.",

author = "Kokum Weeratunga and Anuja Dharmaratne and How, {Khoo Boon}",

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language = "English",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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address = "United States of America",

url = "https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/8014302/proceeding, https://vap.aau.dk/cvsports/3rd-ieee-international-workshop-in-computer-vision-in-sports-at-cvpr-2017/",

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Weeratunga, K, Dharmaratne, A & How, KB 2017, Application of computer vision and vector space model for tactical movement classification in badminton. in Y Liu, J M. Rehg, C J. Taylor & Y Wu (eds), Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 132-138, IEEE International Workshop on Computer Vision in Sports 2017, Honolulu, Hawaii, United States of America, 21/07/17. https://doi.org/10.1109/CVPRW.2017.22

Application of computer vision and vector space model for tactical movement classification in badminton. / Weeratunga, Kokum; Dharmaratne, Anuja; How, Khoo Boon.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. ed. / Yanxi Liu; James M. Rehg; Camillo J. Taylor; Ying Wu. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 132-138 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2017-July).

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

TY - GEN

T1 - Application of computer vision and vector space model for tactical movement classification in badminton

AU - Weeratunga, Kokum

AU - Dharmaratne, Anuja

AU - How, Khoo Boon

N1 - Conference code: 3rd

PY - 2017

Y1 - 2017

N2 - Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.

AB - Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.

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SN - 9781538607343

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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EP - 138

BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017

A2 - Liu, Yanxi

A2 - M. Rehg, James

A2 - J. Taylor, Camillo

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PB - IEEE, Institute of Electrical and Electronics Engineers

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Y2 - 21 July 2017 through 26 July 2017

ER -

Weeratunga K, Dharmaratne A, How KB. Application of computer vision and vector space model for tactical movement classification in badminton. In Liu Y, M. Rehg J, J. Taylor C, Wu Y, editors, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 132-138. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2017.22

Application of computer vision and vector space model for tactical movement classification in badminton

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