Recognizing Walking People

  • Stefan Carlsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)


We present a method for recognition of walking people in monocular image sequences based on extraction of coordinates of specific point locations on the body. The method works by comparison of sequences of recorded coordinates with a library of sequences from different individuals. The comparison is based on the evaluation of view invariant and calibration independent view consistency constraints. These constraints are functions of corresponding image coordinates in two views and are satisfied whenever the two views are projected from the same 3D object. By evaluating the view consistency constraints for each pair of frames in a sequence of a walking person and a stored sequence we get a matrix of consistency values that ideally are zero whenever the pair of images depict the same 3D-posture. The method is virtually parameter free and computes a consistency residual between a pair of sequences that can be used as a distance for clustering and classification. Using interactively extracted data we present experimental results that are superior to those of previously published algorithms both in terms of performance and generality.


structure from motion calibration object recognition 


  1. 1.
    Aggarwal, J.K., Cai, Q., Human Motion Analysis: A Review, CVIU(73), No. 3, March 1999, pp. 428–440.Google Scholar
  2. 2.
    Baumberg A. and Hogg D., Learning flexible models from image sequences, in: J.O. Eklundh, ed., Computer Vision-ECCV’ 94 (Third European Conference on Computer Vision, Stockholm, Sweden, May 2–6, 1994), Volume A, Springer, Berlin, 1994 299–308.Google Scholar
  3. 3.
    Bennett, B.M., Hoffman, D.D., Nicola, J.E., and Prakash, C., Structure from Two Orthographic Views of Rigid Motion, JOSA-A(6), No. 7, July 1989, pp. 1052–1069.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bennett, B.M., Hoffman, D.D., and Prakash, C., Recognition Polynomials, JOSA-A(10), No. 4, April 1993, pp. 759–764.Google Scholar
  5. 5.
    Bregler C. Learning and Recognizing Human Dynamics in Video Sequences IEEE Conf. Computer Vision and Pattern Recognition, June 1997, Puerto RicoGoogle Scholar
  6. 6.
    Carlsson S. and Weinshall D., Dual computation of projective shape and camera positions from multiple images International Journal of Computer Vision Vol. 27 No 3, 1998Google Scholar
  7. 7.
    Cedras C. and Shah M. A survey of motion analysis from moving light displays, Proceedings, CVPR’ 94, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Seattle, WA, June 21–23, 1994), IEEE Computer Society Press, Los Alamitos, CA, 1994, 214–221.CrossRefGoogle Scholar
  8. 8.
    Clemens D. and Jacobs D. Space and time bounds on model indexing PAMI, (13), 1007–1018 1991Google Scholar
  9. 9.
    Cutting J. and Kozlowski L. “Recognizing friends by their walk: Gait perception without familiarity cues,” Bulletin of the Psychonomic Society, 9:253–356, 1977Google Scholar
  10. 10.
    Gavrila, D.M.], The Visual Analysis of Human Movement: A Survey, CVIU(73), No. 1, January 1999, pp. 82–98.zbMATHGoogle Scholar
  11. 11.
    Hogg D., A program to see a walking person, Image and Vision Computing, 1,(1):5–20, 1993Google Scholar
  12. 12.
    Johansson, G., Visual Motion Perception, SciAmer(232), June 1976, pp. 75–88.Google Scholar
  13. 13.
    Little J. and Boyd J.E. Recognizing People by Their Gait: The Shape of Motion Videre: Volume 1 • Number 2 Winter 1998Google Scholar
  14. 14.
    Niyogi S. A., and Adelson E. H. Analyzing and Recognizing Walking Figures in XYT Proceedings of Computer Vision and Pattern Recognition Seattle, WA; June (1994).Google Scholar
  15. 15.
    Polana R. and. Nelson R., Recognition of nonrigid motion, Proceedings, ARPA Image Understanding Workshop (Monterey, CA, November 13–16, 1994), Morgan Kaufmann, San Francisco, CA, 1994, 1219–1224.Google Scholar
  16. 16.
    Quan L. Invariants of 6 points from 3 uncalibrated images, Proc. 3:rd ECCV, pp. Vol. II 459–470 1994MathSciNetGoogle Scholar
  17. 17.
    Rohr K. Towards model-based recognition of human movements in image sequences, CVGIP-Image Understanding, vol. 59, no. 1, 1994, 94–115.CrossRefGoogle Scholar
  18. 18.
    Weinshall, D. Model-based invariants for 3D vision. Int. J. Comp. Vision, 10(1):27–42, 1993CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Stefan Carlsson
    • 1
  1. 1.Numerical Analysis and Computing ScienceRoyal Institute of Technology(KTH)StockholmSweden

Personalised recommendations