We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Dance Gesture Recognition: A Survey

by Mampi Devi, Sarat Saharia, D.k.bhattacharyya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 5
Year of Publication: 2015
Authors: Mampi Devi, Sarat Saharia, D.k.bhattacharyya
10.5120/21696-4803

Mampi Devi, Sarat Saharia, D.k.bhattacharyya . Dance Gesture Recognition: A Survey. International Journal of Computer Applications. 122, 5 ( July 2015), 19-26. DOI=10.5120/21696-4803

@article{ 10.5120/21696-4803,
author = { Mampi Devi, Sarat Saharia, D.k.bhattacharyya },
title = { Dance Gesture Recognition: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 5 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number5/21696-4803/ },
doi = { 10.5120/21696-4803 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:24.842881+05:30
%A Mampi Devi
%A Sarat Saharia
%A D.k.bhattacharyya
%T Dance Gesture Recognition: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 5
%P 19-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gesture recognition means the identification of different expressions of human body parts to express the idea, thoughts and emotion. It is a multi-disciplinary research area. The application areas of gesture recognition have been spreading very rapidly in our real-life activities including dance gesture recognition. Dance gesture recognition means the recognition of meaningful expression from the different dance poses. Today, research on dance gesture recognition receives more and more attention throughout the world. The automated recognition of dance gestures has many applications. The motive behind this survey is to present a comprehensive survey on automated dance gesture recognition with emphasis on static hand gesture recognition. Instead of whole body movement, we consider human hands because human hands are the most flexible part of the body and can transfer the most meaning. A list of research issues and open challenges is also highlighted.

References
  1. Mitra, S. and Acharya, T. 2007. Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(3):311–324.
  2. Sturman, D. J. and Zeltzer, D. 1994. A survey of glove-based input. Computer Graphics and Applications, IEEE, 14(1):30–39.
  3. Fong, T. , Nourbakhsh, I. and Dautenhahn,K. 2003. A survey of socially interactive robots. Robotics and autonomous systems, 42(3):143–166.
  4. Freeman, W. T. , Tanaka, K. , Ohta,J. And Kyuma, K. 1996. Computer vision for computer games. In Automatic Face and Gesture Recognition, Proceedings of the Second International Conference on, pages 100–105. IEEE.
  5. Pradeep kumar, B. P. , Santhosh, S. Y. , Manjuatha, M. B. 2014. Survey on skeleton gesture recognition provided by kinect. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3.
  6. Moeslund, T. B. and Granum, E. 2001. A survey of computer vision-based human motion capture. Computer Vision and Image understanding, 81(3):231–268.
  7. Wu, Y. and Huang, T. S. 1999. Vision-based gesture recognition: A review. In Gesture-Based Communication in Human-Computer Interaction, volume 1739, pages 103–115. Springer Berlin Heidelberg.
  8. Mozarkar, S. and Warnekar, C. S. 2013 Recognizing Bharatnatyam Mudra using Principles of Gesture Recognition. International Journal of Computer Science and Network, 2(2):46–52.
  9. Heryadi, Y. , Fanany, M. I. and Arymurthy, A. M. 2012. Grammar of dance gesture from bali traditional dance. International Journal of Computer Science Issues (IJCSI), 9(6).
  10. Hariharan, D. , Acharya, T. , and Mitra,S. 2011. Recognizing hand gestures of a dancer. In Pattern recognition and machine intelligence, pages 186–192. Springer.
  11. Starner, T and Pentland, A. 1997. Real-time american sign language recognition from video using hidden markov models. In Motion-Based Recognition, pages 227–243. Springer, 1997.
  12. Maraqa, M. and Abu-Zaite,R. 2008. Recognition of arabic sign language (arsl) using recurrent neural networks. In Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008. First International Conference on the, pages 478–481. IEEE.
  13. Murakami, K. and Taguchi, H. 1991. Gesture recognition using recurrent neural networks. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 237–242. ACM.
  14. Starner, T, Weaver, J. and Pentland, A. 1998. Real-time american sign language recognition using desk and wearable computer based video. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(12):1371–1375,.
  15. Kim, J. , Jang, w. and Bien, Z. 1996. A dynamic gesture recognition system for the korean sign language (ksl). Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(2):354–359.
  16. Cho. M. G 2006 A new gesture recognition algorithm and segmentation method of korean scripts for gesture-allowed ink editor. Information Sciences, 176(9):1290–1303.
  17. Bedregal, B. C. , C. R. Costa, A. and Dimuro, G. P. 2006 Fuzzy rule-based hand gesture recognition. In Artificial Intelligence in Theory and Practice, pages 285–294, Springer.
  18. Campbell, R. , LANDIS, T. , and REGARD, M. 1986 Face recognition and lipreading a neurological dissociation. Brain, 109(3):509–521.
  19. Saha, S, Ghosh, S. Konar, A. and Janarthanan, R. 2013. Identification of odissi dance video using kinect sensor. In Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, pages 1837–1842. IEEE.
  20. Phung, S. L. , Bouzerdoum, A. and Chai Sr, D. 2005. Skin segmentation using color pixel classification: analysis and comparison. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(1):148–154.
  21. Belongie, S. , Carson, C. , Greenspan, H. and Malik, J. 1998 Color-and texture-based image segmentation using em and its application to content-based image retrieval. In Computer Vision, Sixth International Conference on, pages 675–682, University of California, IEEE.
  22. Saha, S. , Banerjee, A. Basu, S. , Konar, A. and Atulya, K. N. 2013. Fuzzy image matching for posture recognition in ballet dance. In Fuzzy Systems (FUZZ), 2013 . IEEE International Conference on, pages 1–8.
  23. Saha, S. , Ghosh, L. , Konar, A. and Janarthanan, R. Fuzzy l membership function based hand gesture recognition for bharatanatyam dance. In Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on, pages 331–335. IEEE, 2013.
  24. Kamavisdar, P. , Saluja, S. and Agrawal, S. 2013. A survey on image classification approaches and techniques. International Journal of Advanced Research in Computer and Communication Engineering, 2(1).
  25. Konar, A. Atulya, K. N. , Saha, S. Shreya Ghosh, S. 2013. Gesture recognition from indian classical dance using kinect sensor. In Fifth International Conference on Computational Intelligence, Communication Systems and Networks
  26. Nussipbekov, A. K. , Amirgaliyev, E. N and Hahn, M. 2014. Kazakh traditional dance gesture recognition. In Journal of Physics: Conference Series, volume 495, page 012036. IOP Publishing.
  27. Hasan, H. and Abdul-Kareem, S. 2014. Static hand gesture recognition using neural networks. Artificial Intelligence Review, 41(2):147–181.
  28. Vafaei F. 3013. Taxonomy of Gestures in Human Computer Interaction. PhD thesis, North Dakota State University.
  29. McNeill, D. 1992. Hand and mind: What gestures reveal about thought. University of Chicago Press.
  30. Efron. ,D 1941 Gesture and environment: A tentative study of some of the spatio-temporal and" linguistic" aspects of the gestural behavior of eastern Jews and southern Italians in New York city, living under similar as well as different environmental conditions. King's crown Press, New York.
  31. Canada Kendon A. , Potyados, F. 1988. How gestures can become like words. cross cultural perspectives in nonverbal communication. pages 131–141.
  32. Kahol,K. ,Tripathi, P. and Panchanathan, S. 2004. Automated gesture segmentation from dance sequences. In Automatic Face and Gesture Recognition, In Automatic Face and Gesture Recognition. Proceedings. Sixth IEEE International Conference on, pages 883–888. IEEE.
  33. Raptis, M. , Kirovski, D. and Hoppe, H. 2011. Realtime classification of dance gestures from skeleton animation. In Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 147–156. ACM.
  34. Mallik, A. , Chaudhury, S. and Ghosh, H. 2011. Nrityakosha: Preserving the intangible heritage of indian classical dance. Journal on Computing and Cultural Heritage (JOCCH), 4(3):11.
  35. Minka, T. 2005. A statistical learning/pattern recognition glossary, Retrieved June, 29:2008.
  36. Hong, P. , Turk, M. And Huang, T. S 2000. Gesture modeling and recognition using finite state machines. In Automatic Face and Gesture Recognition, 2000. Proceedings Fourth IEEE International Conference on, pages 410–415. IEEE.
  37. Ibraheem, N. A. and Khan, R. Z. 2012. Vision based gesture recognition using neural networks approaches: A review. International Journal of human Computer Interaction (IJHCI), 3(1):1–14.
  38. Rajko,S. and Qian, G. 2005. A hybrid HMM/DPA adaptive gesture recognition method. In Advances in Visual Computing, pages 227–234. Springer.
  39. Sharma, A. Recognising bharatanatyam dance sequences using rgb-d data. Master's thesis, IIT, Kanpur.
Index Terms

Computer Science
Information Sciences

Keywords

Gesture hand gesture automated dance gesture vision-based gestures glove-based gestures.

Powered by PhDFocusTM