한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제15권4호
  • /
  • Pages.417-424
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  • 2012
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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Dynamic Human Activity Recognition Based on Improved FNN Model

  • Xu, Wenkai (Department of Information & Communications Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Department of Information & Communications Engineering, Tongmyong University)
  • 투고 : 2011.04.14
  • 심사 : 2012.03.31
  • 발행 : 2012.04.30
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초록

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

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참고문헌

  1. H.S. Yoon, J. Soh, Y. J. Bae, and H.S. Yang, "Hand Gesture Recognition Using Combined Features of Location, Angle and Velocity," Pattern Recognition, Vol.34, pp. 1491-1501, 2001. https://doi.org/10.1016/S0031-3203(00)00096-0
  2. N. Liu, B. Lovel, and P. Kootsookos, "Evaluation of Hmm Training Algorithms for Letter Hand Gesture Recognition," IEEE Int'l Symp. on Signal Processing and Information Technology, pp. 648-651, 2003.
  3. E. Hunter, J. Schlenzig, and R. Jain, "Posture Estimation in Reduced-Model Gesture Input Systems," International Workshop on Automatic Face-and Gesture- Recognition, pp. 290-295, 1995.
  4. X. Deyou. "A Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG," International Conference on Pattern Recognition, pp. 519-522, 2006.
  5. M. Elmezain, A. Al-Hamadi, and B. Michaelis. "Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences,"Journal of W.S.C.G., Vol.16, No.1, pp. 65-72, 2008.
  6. E. Holden, R. Owens, and G. Roy. "Hand Movement Classification Using Adaptive Fuzzy Expert System," Journal of Expert System, , Vol.9, No.4, pp. 465-480, 1996.
  7. L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. of the IEEE, Vol.77, No.2, pp. 257-286, 1989. https://doi.org/10.1109/5.18626
  8. Jun Cheng, Pengcheng Li, Guang Chen, Can Xie, and Qi Xie, "View Invariant Hand Gesture Recognition using 3D Trajectory," Proceeding of the World Congress on Intelligent Control and Automation, pp. 6315-6320, 2010.
  9. Forsyth D. A. and Ponce J., Computer Vision: A Modern Approach, Prentice hall, New Jersey, USA, 2002.
  10. L. Nianjun, C. L. Brian, J. K. Peter, and A. D. Richard. "Model Structure Selection & Training Algorithms for a HMM Gesture Recognition System," International Workshop on Frontiers in Handwriting Recognition, pp. 100-106, 2004.
  11. Wenkai Xu and Eung-Joo Lee, "Continuous Gesture Trajectory Recognition System Based on Computer Vision," An International Journal of Applied Mathematics & Information Sciences, Vol. 6, No.2S, pp.339S-346S, 2012.
  12. Lawrence R. Rabiner. "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of the IEEE, Vol.77, No.2, pp. 257-286, 1989. https://doi.org/10.1109/5.18626
  13. Wenkai Xu and Eung-Joo Lee, "Hand Gesture Recognition Using Improved Hidden Markov Models," Journal of Korea Multimedia Society, Vol.14, No.7, pp. 866-871, 2011. https://doi.org/10.9717/kmms.2011.14.7.866

피인용 문헌

  1. Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device vol.16, pp.11, 2013, https://doi.org/10.9717/kmms.2013.16.11.1338