Expanding Irregular Graph Pyramid for an Approaching Object

  • Luis A. Mateos
  • Dan Shao
  • Walter G. Kropatsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


This paper focus on one of the major problems in model-based object tracking, the problem of how to dynamically update the model to adapt changes in the structure and appearance of the target object. We adopt Irregular Graph Pyramids to hierarchically represent the topological structure of a rigid moving object with multiresolution, making it possible to add new details observed from an approaching object by expanding the pyramid.


irregular graph pyramid adaptive representation object structure tracking model 


  1. 1.
    Tang, F., Tao, H.: Probabilistic Object Tracking With Dynamic Attributed Relational Feature Graph. IEEE Trans. Circuits Syst. Video Techn. 18(8), 1064–1074 (2008)CrossRefGoogle Scholar
  2. 2.
    Collins, R., Liu, Y., Leordeanu, M.: On-Line Selection of Discriminative Tracking Features. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(10), 1631–1643 (2005)CrossRefGoogle Scholar
  3. 3.
    Matthews, I., Ishikawa, T., Baker, S.: The template Update Problem. IEEE Transaction Pattern Analysis and Machine Intelligence 26(6), 810–815 (2004)CrossRefGoogle Scholar
  4. 4.
    Lopez Marmol, S.B., Artner, N.M., Iglesias, M., Kropatsch, W., Clabian, M., Burger, W.: Improving Tracking Using Structure. In: proceedings of Computer Vision Winter Workshop (CVWW 2008), February 4–6, pp. 69–76. Moravske Toplice, Slovenia (2008)Google Scholar
  5. 5.
    Gomila, C., Meyer, F.: Graph-based object tracking. In: ICIP 2003, vol. 2, 3, p. 2. Thomson Inc. - Corporate Res, Princeton (2003)Google Scholar
  6. 6.
    Conte, D., Foggia, P., Jolion, J.-M., Vento, M.: A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions. Pattern Recognition 39(4), 562–572 (2006)zbMATHCrossRefGoogle Scholar
  7. 7.
    Kropatsch, W.G.: Building irregular pyramids by dual-graph contraction. In: IEE Proceedings- Vision Image and Signal Processing, vol. 142(6), pp. 366–374 (1995)Google Scholar
  8. 8.
    Brun, L., Kropatsch, W.G.: Dual Contraction of Combinatorial Maps. Technical Report PRIP-TR-54 Institute f. Computer Aided Automation 183/2, Pattern Recognition and Image Processing Group, TU Wien, Austria (1999a)Google Scholar
  9. 9.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, September 1999, pp. 1150–1157 (1999)Google Scholar
  10. 10.
    Nacken, P.F.M.: Image segmentation by connectivity preserving relinking in hierarchical graph structures. Pattern Recognition 28(6), 907–920 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis A. Mateos
    • 1
  • Dan Shao
    • 1
  • Walter G. Kropatsch
    • 1
  1. 1.Pattern Recognition and Image Processing GroupVienna University of TechnologyViennaAUSTRIA

Personalised recommendations