Advertisement

Automatic Bharatnatyam Dance Posture Recognition and Expertise Prediction Using Depth Cameras

  • Pooja VenkateshEmail author
  • Dinesh Babu Jayagopi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Bharatnatyam is an ancient Indian Classical Dance form consisting of complex postures and movements. One main challenge which has not been addressed till now in the intelligent systems community is to perform pose recognition for the basic postures of this dance form called the Bhangas and use this for expertise prediction. In this paper, pose recognition is performed for some important postures in Bharatnatyam in order to find the origin of these postures from the Bhangas and further use this result to predict the expertise of a Bharatnatyam dancer. The features extracted are 10 joint angles using 15 joint locations to predict the 22 postures derived from the basic postures (Bhangas). Support Vector Machine classifier with a radial basis function kernel performed the best for pose recognition. By performing stick figure analysis and grouping of labels we estimate the origin of each of these postures from the Bhangas. This is followed by verification of the grouping using Hamming distance calculation. Testing is done on our own Bharatnatyam dataset consisting of 102 dancers, achieving an accuracy of 87.14%. Expertise prediction of the dancers for the 22 poses was performed for four ratings - Excellent, Good, Satisfactory and Poor giving an accuracy of 68.46% without grouping of postures and 80.80% with grouping of postures.

Keywords

Bhangas Depth cameras Support vector machines Logistic regression Posture recognition 

Notes

Acknowledgement

I would like to thank the all the students of “Nritya Kuteera” and it’s founder Ms. Deepa Bhat for helping with data collection and providing extraordinary support and guidance. Special thanks to Ms. Ambika Shivaramu for easing the data collection challenge with her expertise.

References

  1. 1.
    Sinha, A., Chakravarty, K., Bhowmick, B.: Person identification using skeleton information from kinect. In: Proceedings of the International Conference on Advances in Computer-Human Interactions (2013)Google Scholar
  2. 2.
    Samanta, S., Purkait, P., Chanda, B.: Indian classical dance classification by learning dance pose bases. In: 2012 IEEE Workshop Applications of Computer Vision (WACV) (2012)Google Scholar
  3. 3.
    Weng, E.-J., Fu, L.-C.: On-line human action recognition by combining joint tracking and key pose recognition. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)Google Scholar
  4. 4.
    Damle, R., et al.: Human body skeleton detection and tracking. Int. J. Tech. Res. Appl. 3(6), 222–225 (2015). e-ISSN: 2320-8163. www.ijtra.com Google Scholar
  5. 5.
    Ouyang, Y., Zhang, S.: Human Pose tracking algorithm based on skeleton-texture model. In: Future Computer and Communication, 2009, FCC 2009. IEEE (2009)Google Scholar
  6. 6.
    Zainordin, F.D., et al.: Human pose recognition using Kinect and rule-based system. In: World Automation Congress (WAC). IEEE (2012)Google Scholar
  7. 7.
    Hassan, E., Chaudhury, S., Gopal, M.: Annotating dance posture images using multi kernel feature combination. In: Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE (2011)Google Scholar
  8. 8.
    Jadhav, S., Joshi, M., Pawar, J.: Art to SMart: an evolutionary computational model for BharataNatyam choreography. In: 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE (2012)Google Scholar
  9. 9.
    Villaroman, N., Rowe, D., Swan, B.: Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor. In: Proceedings of the 2011 Conference on Information Technology Education. ACM (2011)Google Scholar
  10. 10.
    Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.International Institute of Information TechnologyBangaloreIndia

Personalised recommendations