Skip to main content

A Trajectory-Based Point Tracker Using Chaos Evolutionary Programming

  • Conference paper
Book cover Next-Generation Applied Intelligence (IEA/AIE 2009)

Abstract

A trajectory-based point tracker using chaos evolutionary programming (CEP) algorithm is proposed in this paper. While motion constraints such as rigidity and small motion which are imposed by previous approaches are liberated, the proposed CEP is proved to be effective for establishing point correspondence between two consecutive frames sampled at a fixed interval. The whole point trajectory within the sample interval is then reconstructed by polynomial interpolation. Our experimental results demonstrate that the proposed point tracker can accurately locate target under different kinds of situations like object deformation, occlusion, and sudden motion as well.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wang, Y., Baciu, G.: Human motion estimation from monocular image sequence based on cross-entropy regularization. Pattern Recognition Letters 24, 315–325 (2003)

    Article  MATH  Google Scholar 

  2. Mezouar, Y., Chaumette, F.: Model-free optimal trajectories in the image space: application to robot vision control. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2001)

    Google Scholar 

  3. Sturm, P.: Structure and motion for dynamic scenes - the case of points moving in planes. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 867–882. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  4. Rangarajan, K., Shah, M.: Establishing motion correspondence. CVGIP: Image Understanding 54, 56–73 (1991)

    Article  MATH  Google Scholar 

  5. Mehrotra, R.: Establishing motion-based feature point correspondence. Pattern Recognition 31, 23–30 (1998)

    Article  Google Scholar 

  6. Salari, V., Sethi, I.K.: Feature point correspondence in the presence of occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 87–91 (1990)

    Article  Google Scholar 

  7. Sethi, I.K., Jain, R.: Finding trajectories of feature points in a monocular image sequence. IEEE Trans. Pattern Anal. Mach. Intell. 9, 56–73 (1987)

    Article  Google Scholar 

  8. Wang, Y.: Feature point correspondence between consecutive frames based on genetic algorithm. International Journal of Robotics and Automation 21, 35–38 (2006)

    Article  Google Scholar 

  9. Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1999)

    MATH  Google Scholar 

  10. Guo, S.M., Shieh, L.S., Chen, G., Coleman, N.P.: Observer-type kalman innovation filter for uncertain linear systems. IEEE Trans. Aerospace Elec. Syst. 37, 1406–1418 (2001)

    Article  Google Scholar 

  11. Li, B., Jiang, W.S.: Optimizing complex functions by chaos search. Cybernetics and Systems 29, 409–419 (1998)

    Article  MATH  Google Scholar 

  12. Yan, X.F., Chen, D.Z., Hu, S.X.: Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model. Computers and Chemical Engineering 27, 1393–1404 (2003)

    Article  Google Scholar 

  13. Halton, J.H.: On the efficiency of certain quasi-random sequences of points in evaluating multidimensional Integrals. Numerische Mathematik 2, 84–90 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  14. Van Der Corput, J.C.: Verteilungsfunktionen. Proc. Kon. Akad. Wet. Amsterdam 38, 1058–1066 (1935)

    MATH  Google Scholar 

  15. Guo, S.M., Liu, K.T., Tsai, J.S.H., Shieh, L.S.: An observer-based tracker for hybrid interval chaotic systems with saturating inputs: The chaos-evolutionary-programming approach. Computers and Mathematics with Applications 55, 1225–1249 (2008)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guo, SM., Hsu, CY., Wu, PN., Tsai, J.SH. (2009). A Trajectory-Based Point Tracker Using Chaos Evolutionary Programming. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02568-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics