Skip to main content
SpringerLink
Log in

Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming

  • Conference paper
Intelligent Robotics and Applications (ICIRA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5928))

Included in the following conference series:

  • 3405 Accesses

Abstract

This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the FGI classifier significantly.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Aggarwal, J.K., Cai, Q., Liao, W., Sabata, B.: Articulated and elastic non-rigid motion: A review. In: Proc. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 2–14 (1994)

    Google Scholar 

  2. Anderson, D., Luke, R.H., Keller, J.M., Skubic, M.: Modeling human activity from voxel person using fuzzy logic. IEEE Transactions on Fuzzy Systems (accepted for publication, 2009)

    Google Scholar 

  3. Bobick, A.F., Wilson, A.D.: A state based technique for the summarization and recognition of gesture. In: International Conference on Computer Vision, pp. 382–388 (1995)

    Google Scholar 

  4. Chan, C.S., Liu, H., Brown, D.J.: Recognition of human motion from qualitative normalised templates. Journal of Intelligent and Robotic Systems 48(1), 79–95 (2007)

    Article  Google Scholar 

  5. Chang, C.S., Liu, H.: Fuzzy qualitative human motion analysis. IEEE Transactions on Fuzzy Systems 17(4), 851–862 (2009)

    Article  Google Scholar 

  6. Darby, J., Li, B., Costen, N.: Human activity recognition: Enhancement for gesture based game interfaces. In: 3rd Int. Conf. on Games Research and Development, CyberGames (2007)

    Google Scholar 

  7. Devi, B.B., Sarma, V.V.S.: Estimation of fuzzy memberships from histograms. Inf. Sci. 35(1), 43–59 (1985)

    Article  MATH  Google Scholar 

  8. Favre, J., Aissaoui, R., Jolles, B.M., Siegrist, O., de Guise, J.A., Aminian, K.: 3d joint rotation measurement using mems inertial sensors: Application to the knee joint. In: ISB-3D: 3-D Analysis of Human Movement, Valenciennes, France, June 28-30 (2006)

    Google Scholar 

  9. Frantti, T.: Timing of fuzzy membership functions from data. Academic Dissertation - University of Oulu - Finland (July 2001)

    Google Scholar 

  10. Guo, Y., Xu, G., Tsuji, S.: Understanding human motion patterns. In: International Conference on Pattern Recognition, pp. B:325–329 (1994)

    Google Scholar 

  11. Iokibe, T.: A method for automatic rule and membership function generation by discretionary fuzzy performance function and its application to a practical system. In: Proceedings of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference, pp. 363–364 (1994)

    Google Scholar 

  12. Khoury, M.: pystep or python strongly typed genetic programming, http://pystep.sourceforge.net/

  13. Khoury, M., Liu, H.: Fuzzy qualitative gaussian inference: Finding hidden probability distributions using fuzzy membership functions. In: IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2009 (2009)

    Google Scholar 

  14. Kim, C., Russell, B.: Automatic generation of membership function and fuzzy rule using inductive reasoning. In: Third International Conference on Industrial Fuzzy Control and Intelligent Systems, 1993. IFIS 1993, pp. 93–96 (1993)

    Google Scholar 

  15. Kim, J., Seo, J., Kim, G.: Estimating membership functions in a fuzzy network model for part-of-speech tagging. Journal of Intelligent and Fuzzy Systems 4, 309–320 (1996)

    Google Scholar 

  16. Lawrence, N.D.: Mocap toolbox for matlab/, http://www.cs.man.ac.uk/~neill/mocap

  17. Liu, H.: A fuzzy qualitative framework for connecting robot qualitative and quantitative representations. IEEE Transactions on Fuzzy Systems 16(8), 1522–1530 (2008)

    Article  Google Scholar 

  18. Liu, H., Brown, D.J., Coghill, G.M.: Fuzzy qualitative robot kinematics. IEEE T. Fuzzy Systems 16(3), 808–822 (2008)

    Article  Google Scholar 

  19. Nieradka, G., Butkiewicz, B.S.: A method for automatic membership function estimation based on fuzzy measures,’. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 451–460. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Pentland, A.P., Oliver, N., Brand, M.: Coupled hidden markov models for complex action recognition. In: Massachusetts Institute of Technology, Media Lab (1996)

    Google Scholar 

  21. Remagnino, P., Tan, T.N., Baker, K.D.: Agent orientated annotation in model based visual surveillance. In: International Conference on Computer Vision, pp. 857–862 (1998)

    Google Scholar 

  22. Sanghi, S.: Determining membership function values to optimize retrieval in a fuzzy relational database. In: Proceedings of the 2006 ACM SE Conference, vol. 1, pp. 537–542 (2006)

    Google Scholar 

  23. Simon, D.: H infinity estimation for fuzzy membership function optimization. Int. J. Approx. Reasoning 40(3), 224–242 (2005)

    Article  MATH  Google Scholar 

  24. Thingvold, J.: Biovision bvh format (1999), http://www.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff

  25. Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36(3), 585–601 (2003)

    Article  Google Scholar 

  26. Yacoob, Y., Black, M.: Parameterized modeling and recognition of activities. In: Sixth International Conference on Computer Vision, 1998, pp. 120–127 (1998)

    Google Scholar 

  27. Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using hidden markov model. In: IEEE Computer Vision and Pattern Recognition, pp. 379–385 (1992)

    Google Scholar 

  28. Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1986)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Industrial Research, University of Portsmouth, Portsmouth, PO1 3QL, United Kingdom

    Mehdi Khoury & Honghai Liu

Authors
  1. Mehdi Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Honghai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore

    Ming Xie

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, P. R. China

    Youlun Xiong

  3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, P.R. China

    Caihua Xiong

  4. Institute of Industrial Research, University of Portsmouth, PO1 3QL, Portsmouth, UK

    Honghai Liu

  5. Department of Computer Science and Electrical Engineering, Kumamoto University, 860-8555, Kumamoto, Japan

    Zhencheng Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Khoury, M., Liu, H. (2009). Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming. In: Xie, M., Xiong, Y., Xiong, C., Liu, H., Hu, Z. (eds) Intelligent Robotics and Applications. ICIRA 2009. Lecture Notes in Computer Science(), vol 5928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10817-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10817-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10816-7

  • Online ISBN: 978-3-642-10817-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation