Gait Sequence Analysis Using Frieze Patterns

  • Yanxi Liu
  • Robert Collins
  • Yanghai Tsin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)


We analyze walking people using a gait sequence representation that bypasses the need for frame-to-frame tracking of body parts. The gait representation maps a video sequence of silhouettes into a pair of two-dimensional spatio-temporal patterns that are near-periodic along the time axis. Mathematically, such patterns are called “frieze” patterns and associated symmetry groups “frieze groups”. With the help of a walking humanoid avatar, we explore variation in gait frieze patterns with respect to viewing angle, and find that the frieze groups of the gait patterns and their canonical tiles enable us to estimate viewing direction of human walking videos. In addition, analysis of periodic patterns allows us to determine the dynamic time warping and affine scaling that aligns two gait sequences from similar viewpoints. We also show how gait alignment can be used to perform human identification and model-based body part segmentation.


Gait Pattern Dynamic Time Warping Stride Frequency Silhouette Image Gait Sequence 
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  1. 1.
    B.K.P. Horn. Robot Vision. MIT Press, 1986.Google Scholar
  2. 2.
    C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proc. IEEE Computer Vision and Pattern Recognition, pages 8–15, 1998.Google Scholar
  3. 3.
    Y. Caspi and M. Irani. A step towards sequence-to-sequence alignment. In IEEE Computer Vision and Pattern Recognition, pages II:682–689, 2000.Google Scholar
  4. 4.
    T.J. Cham and J.M. Rehg. A multiple hypothesis approach to figure tracking. In Proc. IEEE Computer Vision and Pattern Recognition, pages II:239–245, 1999.Google Scholar
  5. 5.
    R. Cutler and L. Davis. Robust real-time periodic motion detection, analysis and applications. IEEE Trans on Pattern Analysis and Machine Intelligence, 22(8):781–796, August 2000.Google Scholar
  6. 6.
    E. Ayyappa. Normal human locomotion, part 1: Basic concepts and terminology. In Journal of Prosthetics and Orthotics, volume 9(1), pages 10–17. The American Academy of Orthotists and Prosthetists, 1997.Google Scholar
  7. 7.
    A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In European Conference on Computer Vision, pages 751–767, 2000.Google Scholar
  8. 8.
    M.A. Giese and T. Poggio. Morphable models for the analysis and synthesis of complex motion patterns. International Journal of Computer Vision, 38(1):59–73, June 2000.Google Scholar
  9. 9.
    R. Gross and J. Shi. The CMU motion of body (MoBo) database. Technical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University, 2001.Google Scholar
  10. 10.
    H. Chui and A. Rangarajan. A new algorithm for non-rigid point matching. IEEE Computer Vision and Pattern Recognition, pages 44–51, 2000.Google Scholar
  11. 11.
    D. Hogg. Model-based vision: A program to see a walking person. Image and Vision Computing, 1(1):5–20, 1983.CrossRefGoogle Scholar
  12. 12.
    K. Kanatani. Statistical Optimization for Geometric Computation: Theory and Practice. North-Holland, 1996.Google Scholar
  13. 13.
    K. Kanatani. Comments on ”Symmetry as a Continuous Feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(3):246–247, 1997.CrossRefGoogle Scholar
  14. 14.
    J.J. Little and J.E. Boyd. Recognizing people by their gait: The shape of motion. Videre, 1(2), 1998.Google Scholar
  15. 15.
    F. Liu and R. W Picard. Finding periodicity in space and time. In IEEE International Conference on Computer Vision (ICCV), 1998.Google Scholar
  16. 16.
    Y. Liu and R. T. Collins. A Computational Model for Repeated Pattern Perception using Frieze and Wallpaper Groups. In Computer Vision and Pattern Recognition Conference (CVPR’00), pages 537–544, Los Alamitos, CA, June 2000. IEEE Computer Society Press. (
  17. 17.
    S.A. Niyogi and E.H. Adelson. Analyzing and recognizing walking figures in xyt. In Proc. IEEE Computer Vision and Pattern Recognition, pages 469–474, 1994.Google Scholar
  18. 18.
    S.M. Seitz and C.R. Dyer. View-invariant analysis of cyclic motion. International Journal of Computer Vision, 25:1–23, 1997.CrossRefGoogle Scholar
  19. 19.
    T.N. Tan, G.D. Sullivan, and K.D. Baker. Recognizing objects on the ground-plane. Image and Vision Computing, 12(3):164–172, April 1994.Google Scholar
  20. 20.
    K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In International Conference on Computer Vision, pages 255–261, 1999.Google Scholar
  21. 21.
    H. Zabrodsky, S. Peleg, and D. Avnir. Symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12):1154–1165, December 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Yanxi Liu
    • 1
  • Robert Collins
    • 1
  • Yanghai Tsin
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA

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