Skip to main content
SpringerLink
Log in

Fuzzy Qualitative Human Motion Analysis

  • Chapter
  • First Online:
Human Motion Sensing and Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 675))

  • 1016 Accesses

Abstract

This chapter proposes a fuzzy qualitative (FQ) approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining FQ robot kinematics with human motion tracking and recognition algorithms. First, a data quantisation process is proposed to relax the computational complexity suffered from visual tracking algorithms. Secondly, a novel human motion representation, Qualitative Normalised Template (QNT), is developed in terms of the FQ robot kinematics framework to effectively represent human motion. The human skeleton is modelled as a complex kinematic chain, its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional Hidden Markov Model (HMM) and Fuzzy Hidden Markov Model (FHMM) shows that the proposed approach consistently outperforms both hidden Markov models in human motion recognition.

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 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Chern Hong Lim, Ekta Vats, and Chee Seng Chan. Fuzzy human motion analysis: A review. Pattern Recognition, 48(5):1773–1796, 2015.

    Article  Google Scholar 

  2. M. Isard and A. Blake. CONDENSATION conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998.

    Article  Google Scholar 

  3. Z. Jing and S. Sclaroff. Segmenting foreground objects from a dynamic textured background via a robust kalman filter. In Proceedings of the Ninth IEEE International Conference on Computer Vision, page 44, Washington, DC, USA, 2003.

    Google Scholar 

  4. Li Yuan, Ai Haizhou, T. Yamashita, Lao Shihong, and M. Kawade. Tracking in low frame rate video: A cascade particle filter with discriminative observers of different lifespans. In Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1–8, Washington, DC, USA, 2007. IEEE Computer Society.

    Google Scholar 

  5. H. Buxton. Learning and understanding dyanmic scene activity. Image and Vision Computing, 21(1):125–136, 2003.

    Article  Google Scholar 

  6. A.F. Bobick and J. Davis. Real-time recognition of activity using temporal templates. In Proceedings of IEEE CS Workshop on Applications of Computer Vision, pages 39–42, 1996.

    Google Scholar 

  7. G. Bradski and J. Davis. Motion segmentation and pose recognition with motion history gradients. Machine Vision and Applications, 13(3):174–184, July 2002.

    Article  Google Scholar 

  8. Z. Chen and H.J. Lee. Knowledge-guided visual perception of 3-d human gait from a single image sequence. IEEE Transactions on Systems, Man and Cybernetics, 22:336–342, 1992.

    Article  Google Scholar 

  9. S.X. Ju, M.J. Black, and Y. Yacoob. Cardboard people: A parameterized model of articulated image motion. In Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition, pages 38–44, Killington, Vermont, USA, 1996.

    Google Scholar 

  10. I.A. Karaulova, P.M. Hall, and A.D. Marshall. A hierachical model of dynamics for tracking people with a single video camera. In Proceedings of the British Machine Vision Conference, 2000.

    Google Scholar 

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

    Google Scholar 

  12. L. Wang, T. Tan, H. Ning, and W. Hu. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12):1505–1518, 2003.

    Article  Google Scholar 

  13. A. Sundaresan, R. Chellappa, and A. Roy Chowdhury. Multiple view tracking of humans modelled by kinematic chains. In Proceedings of the International Conference on Image Processing, volume 2, pages 1009–1012, Singapore, 2004.

    Google Scholar 

  14. H. Sidenbladh, M.J. Black, and D.J. Fleet. Stochastic tracking of 3D human figures using 2D image motion. Proceedings of the European Conference on Computer Vision-Part II, pages 702–718, 2000.

    Google Scholar 

  15. F. Lv and R. Nevatia. Recognition and segmentation of 3-D human action using HMM and multi-class AdaBoost. Proceedings of European Conference on Computer Vision, 4:359–372, 2006.

    Google Scholar 

  16. M. Leo, T. D’Orazio, I. Gnoni, P. Spagnolo, and A. Distante. Complex human activity recognition for monitoring wide outdoor environments. In Proceedings of the International Conference on Pattern Recognition, volume 4, pages 913–916. IEEE, 2004.

    Google Scholar 

  17. C. Bregler. Learning and recognizing human dynamics in video sequences. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, page 568, Washington, DC, USA, 1997.

    Google Scholar 

  18. VI Pavlovic, R. Sharma, and TS Huang. Visual interpretation of hand gestures for human-computerinteraction: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):677–695, 1997.

    Article  Google Scholar 

  19. H. Liu, D.J. Brown, and G.M. Coghill. Fuzzy qualitative robot kinematics. IEEE Transactions on Fuzzy Systems, 16(3):802–822, 2008.

    Google Scholar 

  20. L. Wang, W. Hu, and T. Tan. Recent developments in human motion analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 34(3):334–352, 2004.

    Article  Google Scholar 

  21. J.K. Aggarwal and Q. Cai. Human motion analysis: A review. Computer Vision and Image Understanding, 73(3):428–440, 1999.

    Article  Google Scholar 

  22. T.B. Moeslund and E. Granum. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 81(3):231–268, 2001.

    Article  MATH  Google Scholar 

  23. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Action as space-time shapes. In Proceedings of IEEE International Conference on Computer Vision, volume 2, pages 1395–1402, Beijing, China, 2005.

    Google Scholar 

  24. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In Proceedings of the International Conference on Pattern Recognition, volume 3, pages 32–36, Hong Kong, 2004.

    Google Scholar 

  25. H. Liu and G.M. Coghill. Fuzzy qualitative trigonometry. Proc. IEEE International Conference on Systems, Man and Cybernetics, Hawaii, USA., 2005.

    Google Scholar 

  26. Q. Shen and R. Leitch. Fuzzy qualitative simulation. IEEE Transactions on Systems, Man, and Cybernetics, 23(4):1038–1061, 1993.

    Article  Google Scholar 

  27. A.F. Bobick and A. Wilson. A state-based technique for the summarization and recognition of gesture. In Proceeding of the International Conference on Computer Vision, pages 382–388, 1995.

    Google Scholar 

  28. Richard M. Murray, Shankar Shastry, Zexiang Li, and S. Shankar Sastry. A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.

    Google Scholar 

  29. H. Liu and D.J. Brown. An extension to fuzzy qualitative trigonometry and its application to robot kinematics. In Proceedings of the IEEE International Conference on Fuzzy Systems, pages 1111–1118, 2006.

    Google Scholar 

  30. G. Johansson. Visual motion perception. Scientific American, 232(6):76–88, 1975.

    Article  Google Scholar 

  31. L.R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–284, February 1989.

    Article  Google Scholar 

  32. M.A. Mohamed and P. Gader. Generalized hidden markov models. i. theoretical frameworks. IEEE Transaction on Fuzzy Systems, 8(1):67–81, 2000.

    Google Scholar 

  33. H. Qiang and C. Debrunner. Individual recognition from periodic activty using hidden markov models. In Proceedings of the Workshop on Human Motion, pages 47–52, 2000.

    Google Scholar 

  34. M.A. Mohamed and P. Gader. Generalized hidden markov models. ii. application to handwritten word recognition. IEEE Transaction on Fuzzy Systems, 8(1):82–94, 2000.

    Google Scholar 

  35. M.T. Johnson. Capacity and complexity of hmm duration modelling techniques. IEEE Transactions on Signal Processing Letters, 12(5):407–410, 2005.

    Article  MathSciNet  Google Scholar 

  36. H. Liu. A fuzzy qualitative framework for connecting robot qualitative and quantitative representations. IEEE Transactions on Fuzzy Systems, 16(6):1522–1530, 2008.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Honghai Liu & Zhaojie Ju

  2. School of Automation, Shenyang Aerospace University, Shenyang, China

    Xiaofei Ji

  3. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  4. Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Mehdi Khoury

Authors
  1. Honghai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaojie Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofei Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chee Seng Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mehdi Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honghai Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer-Verlag GmbH Germany

About this chapter

Cite this chapter

Liu, H., Ju, Z., Ji, X., Chan, C.S., Khoury, M. (2017). Fuzzy Qualitative Human Motion Analysis. In: Human Motion Sensing and Recognition. Studies in Computational Intelligence, vol 675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53692-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-53692-6_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53690-2

  • Online ISBN: 978-3-662-53692-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation