Advertisement

Real-Time Tracking of Multiple Articulated Structures in Multiple Views

  • Tom Drummond
  • Roberto Cipolla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)

Abstract

This paper describes a highly flexible approach to real-time frame-rate tracking in complex camera and structures configurations, including the use of multiple cameras and the tracking of multiple or articulated targets. A powerful and general method is presented for expressing and solving the constraints which exist in these configurations in a principled manner. This method exploits the geometric structure present in the Lie group and Lie algebra formalism to express the constraints that derive from structures such as hinges or a common ground plane. This method makes use of the adjoint representation to simplify the constraints which are then applied by means of Lagrange multipliers.

Keywords

Visual Servoing Multiple Camera Rigid Component Euclidean Transformation Video Feed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    T. Drummond and R. Cipolla. Real-time tracking of complex structures for visual servoing. In PreProceedings of Vision Algorithms: Theory and Practice, pages 91–98, Corfu, Greece, 21–22 September 1999. Also to appear in Springer Lecture Notes in Computer Science.Google Scholar
  2. [2]
    C. Harris. Geometry from visual motion. In A. Blake, editor, Active Vision, chapter 16, pages 263–284. MIT Press, 1992.Google Scholar
  3. [3]
    G. Hager, G. Grunwald, and K. Toyama. Feature-based visual servoing and its application to telerobotics. In V. Graefe, editor, Intelligent Robotic Systems. Elsevier, 1995.Google Scholar
  4. [4]
    D. Terzopoulos and R. Szeliski. Tracking with Kalman snakes. In A. Blake, editor, Active Vision, chapter 1, pages 3–20. MIT Press, 1992.Google Scholar
  5. [5]
    R. Cipolla and A. Blake. Active Vision, chapter Geometry from Visual Motion, pages 189–202. 1992.Google Scholar
  6. [6]
    D. G. Lowe. Robust model-based motion tracking through the integration of search and estimation. International Journal of Computer Vision, 8(2):113–122, 1992.CrossRefGoogle Scholar
  7. [7]
    P. Wunsch and G. Hirzinger. Real-time visual tracking of 3-Dobjects with dynamic handling of occlusion. In Proceedings of the 1997 International Conference on Robotics and Automation, pages 2868–2873, 1997.Google Scholar
  8. [8]
    C. Harris. Tracking with rigid models. In A. Blake, editor, Active Vision, chapter 4, pages 59–73. MIT Press, 1992.Google Scholar
  9. [9]
    M. Isard and A. Blake. CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998.CrossRefGoogle Scholar
  10. [10]
    N. Daucher, M. Dhome, J. T. Lapresté, and G. Rives. Modelled object pose estimation and tracking by monocular vision. In Proceedings of the British Machine Vision Conference, pages 249–258, 1993.Google Scholar
  11. [11]
    A. D. Worrall, G. D. Sullivan, and K. D. Baker. Pose refinement of active models using forces in 3D. In J. Eklundh, editor, Proceedings of the Third European Conference on Computer vision ( ECCV’94 ), volume 2, pages 341–352, May 1994.Google Scholar
  12. [12]
    E. Marchand, P. Bouthemy, F. Chaumette, and V. Moreau. Robust real-time visual tracking using a 2D-3D model-based approach. In Proceedings of ICCV’99, volume 1, pages 262–268, Kerkyra, Greece, 20–27 September 1999.Google Scholar
  13. [13]
    V.S. Varadarajan. Lie Groups, Lie Algebras and Their Representations. Number 102 in Graduate Texts in Mathematics. Springer-Verlag, 1974.Google Scholar
  14. [14]
    D.H. Sattinger and O.L. Weaver. Lie groups and algebras with applications to physics, geometry, and mechanics. Number 61 in Applied Mathematical Sciences. Springer-Verlag, 1986.Google Scholar
  15. [15]
    T. Drummond and R. Cipolla. Visual tracking and control using Lie algebras. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 652–657, Fort Collins, Colorado, 23–25 June 1999. IEEE.Google Scholar
  16. [16]
    M. Armstrong and A. Zisserman. Robust object tracking. In Proceedings of Second Asian Conference on Computer Vision, pages 58–62, 1995.Google Scholar
  17. [17]
    M. Paterson and F. Yao. Efficient binary space partitions for hidden surface removal and solid modeling. Discrete and Computational Geometry, 5(5):485–503, 1990.zbMATHMathSciNetGoogle Scholar
  18. [18]
    J. MacCormick and A. Blake. Spatial dependence in the observation of visual contours. In Proceedings of the Fifth European Conference on Computer vision (ECCV’98), pages 765–781, 1998.Google Scholar
  19. [19]
    M. Haag and H-H. Nagel. Tracking of complex driving manoeuvres in traffic image sequences. Image and Vision Computing, 16:517–527, 1998.CrossRefGoogle Scholar
  20. [20]
    J. K. Aggarwal, Q. Cai, W. Liao, and B. Sabata. Nonrigid motion analysis: articulated and elastic motion. Computer Vision and Image Understanding, 70(2):142–156, 1998.CrossRefGoogle Scholar
  21. [21]
    D. G. Lowe. Fitting parameterised 3-D models to images. IEEE T-PAMI, 13(5):441–450, 1991.MathSciNetGoogle Scholar
  22. [22]
    Q. Delamarre and O. Faugeras. 3D articulated models and multi-view tracking with silhouttes. In Proceedings of ICCV’99, volume 2, pages 716–721, Kerkyra, Greece, 20–27 September 1999.Google Scholar
  23. [23]
    T. Drummond and R. Cipolla. Real-time tracking of complex structures with on-line camera calibration. In Proceedings of British Machine Vision Conference 1999, volume 2, pages 574–583, Nottingham, 13–16 September 1999. BMVA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Tom Drummond
    • 1
  • Roberto Cipolla
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

Personalised recommendations