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

Kokum Weeratunga, Anuja Dharmaratne, Khoo Boon How

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13 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
EditorsYanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781538607336
ISBN (Print)9781538607343
Publication statusPublished - 2017
EventIEEE International Workshop on Computer Vision in Sports 2017 - Honolulu, United States of America
Duration: 21 Jul 201726 Jul 2017
Conference number: 3rd (Proceedings) (Website)

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516


ConferenceIEEE International Workshop on Computer Vision in Sports 2017
Country/TerritoryUnited States of America
Internet address

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