Skip to main content
SpringerLink
Log in

Automatic Learning of Gesture Recognition Model Using SOM and SVM

  • Conference paper
Advances in Visual Computing (ISVC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6453))

Included in the following conference series:

Abstract

In this paper, we propose an automatic learning method for gesture recognition. We combine two different pattern recognition techniques: the Self-Organizing Map (SOM) and Support Vector Machine (SVM). First, we apply the SOM to divide the sample data into phases and construct a state machine. Next, we apply the SVM to learn the transition conditions between nodes. An independent SVM is constructed for each node. Of the various pattern recognition techniques for multi-dimensional data, the SOM is suitable for categorizing data into groups, and thus it is used in the first process. On the other hand, the SVM is suitable for partitioning the feature space into regions belonging to each class, and thus it is used in the second process. Our approach is unique and effective for multi-dimensional and time-varying gesture recognition. The proposed method is a general gesture recognition method that can handle any kinds of input data from any input device. In the experiment presented in this paper, we used two Nintendo Wii Remote controllers, with three-dimensional acceleration sensors, as input devices. The proposed method successfully learned the recognition models of several gestures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Billon, R., Nedelec, A., Tisseau, J.: Gesture recognition in flow based on PCA and using multiagent system. In: Proc. of Advances on Computer Entertainment, pp. 139–146 (2008)

    Google Scholar 

  2. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University, Cambridge (2000)

    MATH  Google Scholar 

  3. Chang, C.-C., Lin, C.-J.: 2002, LIBSVM: a Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html

  4. Irie, K., Wakamura, N., Umeda, K.: Construction of an Intelligent Room Based on Gesture Recognition -Operation of Electric Appliances with Hand Gestures. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 193–198 (2004)

    Google Scholar 

  5. Iuchi, H., Maeda, S., Tsuruta, N.: Gesture Recognition using Self-Organizing Maps and Hidden Markov Model. In: IPSJ SIG Notes, Computer Vision and Image Media, vol. 2001(36), pp. 127–134 (2001)

    Google Scholar 

  6. Joutou, T., Yanai, K.: Web Image Classification with Bag-of-keypoints. IPSJ SIG Notes, Computer Vision and Image Media 2007(42), 201–208 (2007)

    Google Scholar 

  7. Kohonen, T.: The self-organizing map. Proc. of the IEEE 78(9), 1464–1479 (1990)

    Article  Google Scholar 

  8. Liang, X., Li, Q., Zhang, X., Zhang, S., Geng, W.: Performance-driven motion choreographing with accelerometers. Computer Animation and Virtual Worlds 20(2-3), 89–99 (2009)

    Article  Google Scholar 

  9. Matsunaga, T., Masaki, O.: Recognition of Walking Motion Using Support Vector Machine. In: Proc. of ISICE 2007, pp. 337–342 (2007)

    Google Scholar 

  10. Nanri, T., Otsu, N.: Anomaly Detection in Motion Images Containing Multiple Persons. In: Proc. of PRMU 2004-77, vol. 104(291), pp. 583–588 (2004)

    Google Scholar 

  11. Noma, K., Nakai, M., Shimodaira, H., Sagayama, S.: Sequential-Pattern Recognition by Support Vector Machines using Dynamic Time-Alignment Kernels, Technical report of IEICE, vol. 100(507), pp. 63–68 (2000)

    Google Scholar 

  12. Oshita, M.: Motion-Capture-Based Avatar Control Framework in Third-Person View Virtual Environments. In: ACM SIGCHI International Conference on Advance in Computer Entertainment Technology (2006)

    Google Scholar 

  13. Yamada, T., Umeda, K.: Improvement of the Method of Operating a Mobile Robot by Gesture Recognition. In: JSME Conference on Robots and Mechatronics, vol. 2001, p. 49 (2001)

    Google Scholar 

  14. Yoshioka, T., Koga, H., Watanabe, T., Yokoyama, T.: Online Automatic Acquisition of Human Motion Models with Voting, Technical report of IEICE, vol. 105(302), pp. 119–124 (2005)

    Google Scholar 

  15. Toyokura, Y., Nankaku, Y., et al.: Approach to Japanese Sign Language Word Recognition using Basic Motion HMM. In: Proceedings of the Society Conference of IEICE, vol. 2006, p. 72 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan

    Masaki Oshita & Takefumi Matsunaga

Authors
  1. Masaki Oshita
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takefumi Matsunaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammoud

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Oshita, M., Matsunaga, T. (2010). Automatic Learning of Gesture Recognition Model Using SOM and SVM. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_72

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17289-2_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17288-5

  • Online ISBN: 978-3-642-17289-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation