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Ballet Pose Recognition: A Bag-of-Words Support Vector Machine Model for the Dance Training Environment

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Information Science and Applications 2018 (ICISA 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 514))

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Abstract

Serious dance students are always looking for ways in which they can improve their technique by practising alone at home or a studio by using a mirror for feedback. The problem these students face is that for many ballet postures it is difficult to analyze one’s own faults. By not having guidance regarding proper positional alignment, dancers risk developing injuries and bad habits. The proposed solution is a system which recognizes the ballet position being performed by a dancer. After recognition, this research aims to work towards providing the necessary correction as feedback. The results for recognition in the system, using a Bag-of-Words approach to a Support Vector Machine classifier, showed an accuracy of 59.6%. Multiple implementations are produced and assessed in this paper. It is clearly found that the approach is feasible, however, work for improving the accuracy is required. Recommendations to improve effective pose recognition for future work are therefore discussed.

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Author information

Authors and Affiliations

  1. Academy of Computer Science and Software Engineering, University of Johannesburg, Cnr University Road and Kingsway Avenue, APK Campus, Johannesburg, 2006, South Africa

    Margaux Fourie & Dustin van der Haar

Authors
  1. Margaux Fourie
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  2. Dustin van der Haar
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Corresponding author

Correspondence to Dustin van der Haar .

Editor information

Editors and Affiliations

  1. iCatse, Seongnam, Gyeonggi, Korea (Republic of)

    Kuinam J. Kim

  2. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Nakhoon Baek

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© 2019 Springer Nature Singapore Pte Ltd.

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Fourie, M., van der Haar, D. (2019). Ballet Pose Recognition: A Bag-of-Words Support Vector Machine Model for the Dance Training Environment. In: Kim, K., Baek, N. (eds) Information Science and Applications 2018. ICISA 2018. Lecture Notes in Electrical Engineering, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-13-1056-0_33

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  • DOI: https://doi.org/10.1007/978-981-13-1056-0_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1055-3

  • Online ISBN: 978-981-13-1056-0

  • eBook Packages: EngineeringEngineering (R0)

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