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)


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.


Computer vision Pose recognition Dance Ballet industry Automation 



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


  1. 1.
    Nishani, E., Çiço, B.: Computer vision approaches based on deep learning and neural networks: deep neural networks for video analysis of human pose estimation. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO), pp. 1–4. IEEE (2017)Google Scholar
  2. 2.
    Yao, B., Hagras, H., Alhaddad, M.J., Alghazzawi, D.: A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments. Soft Comput. 19(2), 499–506 (2015). Scholar
  3. 3.
    Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. Int. J. Ambient Comput. Intell. (IJACI) 7(2), 75–92 (2016)CrossRefGoogle Scholar
  4. 4.
    Di Orio, L.: Ballet: Method to Method. Dance Informa American Edition (2013)Google Scholar
  5. 5.
    New York Film Academy: Ballet and modern dance: using ballet as the basis for other dance techniques (2014)Google Scholar
  6. 6.
    Clay, A., Domenger, G., Conan, J., Domenger, A., Couture, N.: Integrating augmented reality to enhance expression, interaction & collaboration in live performances: a ballet dance case study. In: 2014 IEEE International Symposium on Mixed and Augmented Reality-Media, Art, Social Science, Humanities and Design, ISMAR-MASH’D, pp. 21–29. IEEE (2014)Google Scholar
  7. 7.
    Royal Academy of Dancing: The Foundations of Classical Ballet Technique. Royal Academy of Dancing (1997)Google Scholar
  8. 8.
    Kassing, G., Jay, D.M.: Beginning Ballet Technique. Human Kinetics (1998)Google Scholar
  9. 9.
    Trajkova, M., Cafaro, F.: E-ballet: designing for remote ballet learning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp 2016, pp. 213–216 (2016)Google Scholar
  10. 10.
    Dance Spirit: Working one-on-one: what to expect from private lessons (2014)Google Scholar
  11. 11.
    Grieg, V.: Inside Ballet Technique. Dance Books (1994)Google Scholar
  12. 12.
    Speck, S., Cisneros, E.: Ballet for Dummies. Wiley, Hoboken (2003)Google Scholar
  13. 13.
    Snyder, A.F.: Securing our dance heritage: issues in the documentation and preservation of danceatio (1999)Google Scholar
  14. 14.
    Gupta, M., Hallam, J., Keen, E., Lee, C., McKenna, A.: Ballet hero: building a garment for memetic embodiment in dance learning. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers Adjunct Program - ISWC 2014 Adjunct, pp. 49–54 (2014)Google Scholar
  15. 15.
    Nakai, M., Tsunoda, Y., Hayashi, H., Murakoshi, H.: Prediction of basketball free throw shooting by OpenPose. In: Kojima, K., Sakamoto, M., Mineshima, K., Satoh, K. (eds.) JSAI-isAI 2018. LNCS (LNAI), vol. 11717, pp. 435–446. Springer, Cham (2019). Scholar
  16. 16.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
  17. 17.
    Dancs, J., Sivalingam, R., Somasundaram, G., Morellas, V., Papanikolopoulos, N.: Recognition of ballet micro-movements for use in choreography. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1162–1167 (2013)Google Scholar
  18. 18.
    Hong, G.-S., Park, S.-W., Park, S.-H., Nasridinov, A., Park, Y.-H.: A ballet posture education using IT techniques: a comparative study. In: Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory, pp. 114–116. ACM (2016)Google Scholar
  19. 19.
    Saha, S., Banerjee, A., Basu, S., Konar, A., Nagar, A.K.: Fuzzy image matching for posture recognition in ballet dance. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2013)Google Scholar
  20. 20.
    Saha, S., Konar, A., Janarthanan, R.: Posture recognition in ballet dance a case study on fuzzy uniform discrete membership function. In: Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), pp. 708–711 (2014)Google Scholar
  21. 21.
    Banerjee, A., Saha, S., Basu, S., Konar, A., Janarthanan, R.: A novel approach to posture recognition of ballet dance. In: 2014 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (2014)Google Scholar
  22. 22.
    Kyan, M., et al.: An approach to ballet dance training through ms kinect and visualization in a cave virtual reality environment. ACM Trans. Intell. Syst. Technol. (TIST) 6(2), 23 (2015)Google Scholar
  23. 23.
    Saha, S., Konar, A.: Topomorphological approach to automatic posture recognition in ballet dance. IET Image Process. 9(11), 1002–1011 (2015)CrossRefGoogle Scholar
  24. 24.
    Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media Inc., Newton (2017) Google Scholar
  25. 25.
    Frossard, D.: VGG in TensorFlow (2016)Google Scholar
  26. 26.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  27. 27.
    Deng, Z., Sun, H., Zhou, S., Zhao, J., Lei, L., Zou, H.: Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J. Photogram. Remote Sens. 145, 3–22 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

Personalised recommendations