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Computer Vision for the Ballet Industry: A Comparative Study of Methods for Pose Recognition

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 389)

Abstract

The presence of computer vision technology is continually expanding into multiple application domains. An industry and an art form that is particularly attractive for the application of computer vision algorithms is ballet. Due to the well-codified poses, along with the challenges that exist within the ballet domain, automation for the ballet environment is a relevant research problem. The paper proposes a model called BaReCo, which allows for ballet poses to be recognised using computer vision methods. The model contains multiple computer vision pipelines which allows for the comparison of approaches that have not been widely explored in the ballet domain. The results have shown that the top-performing pipelines achieved an accuracy rate of 99.375% and an Equal Error Rate (EER) of 0.119% respectively. The study additionally produced a ballet pose dataset, which serves as a contribution to the ballet and computer vision community. By combining suitable computer vision methods, the study demonstrates that successful recognition of ballet poses can be accomplished.

Keywords

Computer vision Pose recognition Dance Ballet industry Automation 

Notes

Acknowledgements

This research benefitted, in part, from support from the Faculty of Science at the University of Johannesburg.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

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