Journal of Computer Science and Technology

, Volume 19, Issue 5, pp 674–683 | Cite as

Parametric tracking of legs by exploiting Intelligent Edge

  • Chum-Hong PanEmail author
  • Hong-Ping Yan
  • Song-De Ma


In this paper the idea of Intelligent scissors is adopted for contour tracking in dynamic image sequence. Tracking contour of human can therefore be converted to tracking seed points in images by making use of the properties of the optimal path (Intelligent Edge). The main advantage of the approach is that it can handle correctly occlusions that occur frequently when human is moving. Non-Uniform Rational B-Spline (NURBS) is used to represent parametrically the contour that one wants to track. In order to track robustly the contour in images, similarity and compatibility measurements of the edge are computed as the weighting functions of optimal estimator. To reduce dramatically the computational load, an efficient method for extracting the region interested is proposed. Experiments show that the approach works robustly for sequences with frequent occlusions.


parametric tracking intelligent scissors intelligent edge occlusion dynamic image sequence 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Aggarwal J, Cai Q. Human motion analysis — A review.Computer Vision and Image Understanding, 1999, 73: 428–440.CrossRefGoogle Scholar
  2. [2]
    Moeslnd T B, Granum E. A survey of computer visionbased human motion capture.Computer Vision and Image Understanding, 2001, 81: 231–236.CrossRefGoogle Scholar
  3. [3]
    Hogg D. Model-based vision: A program to see a walking person.Image and Vision Computing, 1983, 1(1): 5–20.CrossRefGoogle Scholar
  4. [4]
    Niyogi S A, Adelson E H. Analyzing and recognizing walking figures in XYT. InProc. Conf. Computer Vision and Pattern Recognition, 1994, pp. 469–474.Google Scholar
  5. [5]
    Rohr K. Human Movement Analysis Based on Explicit Motion Models. Kluwer Academic, Dordrecht/Boston, 1997, Chapter 8, pp. 171–198.Google Scholar
  6. [6]
    Wachter S, Nagel H-H. Tracking persons in monocular image sequences.Computer Vision and Image Understanding, 1999, 74: 174–192.CrossRefGoogle Scholar
  7. [7]
    MacCormick J, Isard M. Partitioned sampling, articulated objects, and interface-quality hand tracking. InProc. ECCV, Dublin, 2000, 2: 13–19.Google Scholar
  8. [8]
    Kakadiaris I, Metaxas D. Model-based estimation of 3D human motion with occlusion based on active multiviewpoint selection. InProc. Conf. Computer Vision and Pattern Recognition, 1996, pp. 81–87.Google Scholar
  9. [9]
    Deutscher J, Davison A, Reid I. Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. InProc. Conf. Computer Vision and Pattern Recognition, 2001, pp. 187–193.Google Scholar
  10. [10]
    Sullivan J, Blake A, MacCormick J. Object localization by Bayesian correlation. InProc. the 6th European Conference on Computer Vision, Dublin, 2000.Google Scholar
  11. [11]
    Chen Y, Rui Y, Huang T S. JPDAF based HMM for real-time contour tracking. InProc. Conf. Computer Vision and Pattern Recognition, Kauai, Hawaii, 2001, pp. 203–209.Google Scholar
  12. [12]
    Ricquebourg Y, Bouthemy P. Real-time tracking of moving persons by exploiting spatial-temporal image slices.IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22: 797–808.CrossRefGoogle Scholar
  13. [13]
    Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models.Int. J. Computer Vision, 1988, 1: 321–331.CrossRefGoogle Scholar
  14. [14]
    Peterfreund N. Robust tracking of position and velocity with Kalman snakes.IEEE Trans. Pattern Analysis and Machine Intelligence, 1999, 21: 564–569.CrossRefGoogle Scholar
  15. [15]
    Mortensen E N, Barrett W A. Intelligent scissors for image composition. InProc. the ACM SIGGRAPH'95, Los Angeles, CA, USA, 1995, pp. 191–198.Google Scholar
  16. [16]
    Isard M, Blake A. Condensation — Conditional density propagation for visual tracking.International Journal of Computer Vision, 1998, 29: 2–28.CrossRefGoogle Scholar
  17. [17]
    Mundy J L, Zissermann A. Geometric Invariance in Computer Vision. Artificial Intelligence Series, MIT Press, Cambridge, USA, 1992.Google Scholar
  18. [18]
    Wren C R, Azarbaeyjani A, Darrell T, Pentland A P. Pfinder: Real-time tracking of the human body.IEEE Trans. Pattern Analysis and Machine Intelligence, 1997, 19: 780–785.CrossRefGoogle Scholar
  19. [19]
    Suauffer C, Grimson W E L. Learning patterns of activity using real-time tracking.IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22: 747–757.CrossRefGoogle Scholar
  20. [20]
    Deutscher J, Blake A, North B, Bascle B. Tracking through singularities and discontinuities by random sampling. InProc. 7th Int. Conf. Computer Vision, 1999, 2: 1144–1149.Google Scholar
  21. [21]
    Piegl L, Tiller W. The NURES Book, 2nd ed., Springer-Verlag, Berlin, Heidelberg, 1997.Google Scholar
  22. [22]
    Hearn D, Baker M P. Computer Graphics. 2nd ed., Prentice Hall, Englewood Cliffs, NJ, 1994.zbMATHGoogle Scholar
  23. [23]
    Xu G, Zhang Z. Epiploar Geometry in Stereo, Motion and Object Recognition. Kluwer Academic Publisher, Boston, MA, 1996.Google Scholar
  24. [24]
    Saint-Marc P, Rom H, Medioni G. B-spline contour representation and symmetry detection.IEEE Trans. Pattern Analysis and Machine Intelligence, 1993, 15: 1191–1197.CrossRefGoogle Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc., Beijing China and Allerton Press Inc. 2004

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationThe Chinese Academy of SciencesBeijingP.R. China

Personalised recommendations