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Hand Detection and Gesture Recognition Exploit Motion Times Image in Complicate Scenarios

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Advances in Visual Computing (ISVC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6454))

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Abstract

Hand gesture recognition in complicate scenario is still a challenging problem in computer vision domain. In this paper, a novel hand gesture recognition system is presented. To detect the exact hand target from complicate scenarios, the color and motion clues are used to obtain potential hand regions. And then a method named Motion Times Image (MTI) is proposed to identify the optimal hand location. The R-transform descriptor is used to describe the hand shape features and an offline trained Support Vector Machine with Radial Basis Function kernels (RBF-SVM) is exploited to perform the hand gesture recognition task. Extensive experiments with different users under dynamic and complicate scenarios are conducted to show its high recognition accuracy and strong robustness.

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References

  1. Wu, Y., Lin, J., Huang, T.S.: Analyzing and Capturing Articulated Hand Motion in Image Se quences. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(12), 1910–1922 (2005)

    Article  Google Scholar 

  2. Mistry, P., Maes, P., Chang, L.: WUW - Wear Ur World: A Wearable Gestural Interface. In: Proceedings of the 27th international Conference Extended Abstracts on Human Factors in Computing Systems, pp. 4111–4116. ACM, New York (2009)

    Google Scholar 

  3. Yang, M.H., Ahuja, N., Tabb, M.: Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8), 1061–1074 (2002)

    Article  Google Scholar 

  4. Chen, Y.T., Tseng, K.T.: Multiple-angle Hand Gesture Recognition by Fusing SVM Classifiers. In: IEEE conference on Automation Science and Engineering, Scottsdale, AZ, USA, pp. 527–530 (2007)

    Google Scholar 

  5. Amin, M.A., Yan, H.: Sign Language Finger Alphabet Recognition from Gabor-PCA Representation of Hand Gestures. In: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 2218–2223 (2007)

    Google Scholar 

  6. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-time Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(12), 2247–2253 (2007)

    Article  Google Scholar 

  7. Stenger, B., Woodley, T., Kim, T.K., Hernández, C., Cipolla, R.: AIDIA - Adaptive Interface for Display Interaction. In: Proceedings of British Machine Vision Conference, Leeds (2008)

    Google Scholar 

  8. Shin, J.H., Lee, J.S., Kil, S.K., Shen, D.F., Ryu, J.G., Lee, E.H., Min, H.K., Hong, S.H.: Hand Region Extraction and Gesture Recognition Using Entropy Analysis. Proceedings of International Journal of Computer Science and Network Security 6(2), 216–222 (2006)

    Google Scholar 

  9. Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Model-based Hand Tracking Using a Hierarchical Bayesian Filter. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 1372–1384 (2006)

    Article  MATH  Google Scholar 

  10. Tabbone, S., Wendling, L., Salmon, J.-P.: A New Shape Descriptor Defined on the Radon Transform. Computer Vision and Image Understanding 102(1), 42–51 (2006)

    Article  Google Scholar 

  11. Vassili, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-Based Skin Color Detection Techniques. In: Proc. Graphicon 2003, pp. 85–92 (2003)

    Google Scholar 

  12. Xiong, Y., Fang, B., Quek, F.: Extraction of Hand Gestures with Adaptive Skin Color Models and Its Applications to Meeting Analysis. In: Proceedings of the Eighth IEEE International Symposium on Multimedia, pp. 647–651 (2006)

    Google Scholar 

  13. Hsu, R.-L., Mottleb, M.A., Jain, A.K.: Face Detection in Color Images. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)

    Article  Google Scholar 

  14. Rosin, P.: Thresholding for Change Detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 274–279 (1998)

    Google Scholar 

  15. Bobick, A.F., Davis, J.W.: The Recognition of Human Movement Using Temporal Templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)

    Article  Google Scholar 

  16. http://sourceforge.net/projects/opencvlibrary (2010)

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Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

    Zhan Song, Hanxuan Yang, Yanguo Zhao & Feng Zheng

  2. The Chinese University of Hong Kong, Hong Kong, China

    Zhan Song, Hanxuan Yang, Yanguo Zhao & Feng Zheng

Authors
  1. Zhan Song
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  2. Hanxuan Yang
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  3. Yanguo Zhao
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  4. Feng Zheng
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Editor information

Editors and Affiliations

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

    George Bebis

  2. Moffett Field, NASA Ames Research Center, 94035, 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 Hammound

  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

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© 2010 Springer-Verlag Berlin Heidelberg

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Song, Z., Yang, H., Zhao, Y., Zheng, F. (2010). Hand Detection and Gesture Recognition Exploit Motion Times Image in Complicate Scenarios. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_61

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  • DOI: https://doi.org/10.1007/978-3-642-17274-8_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17273-1

  • Online ISBN: 978-3-642-17274-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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