Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A fuzzy inference approach to template-based visual tracking

  • 173 Accesses

  • 1 Citations

Abstract

The tracking of visual features using appearance models is a well studied but still open area of computer vision. In the absence of knowledge about the structural constraints of the tracked object, the validity of the model can be compromised if only appearance information is used. We propose a fuzzy inference scheme that can be used to selectively update a given template-based model in tracking tasks. This allows us to track moving objects under translation, rotation, and scale changes with minimal feature drift. Moreover, no rigidity constraint needs to be enforced on the moving target. Some experiments have been performed using several targets, and the results are very close to the ground truth paths. The computational cost of our approach is low enough to allow its application in real-time tracking using modest hardware requirements.

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

References

  1. 1

    Chaumette F.: Image moments: a general and useful set of features for visual servoing. IEEE Trans. Robot. 20(4), 713–723 (2004)

  2. 2

    Bigot J., Gadat S., Loubes J.-M.: Statistical M-estimation and consistency in large deformable models for image warping. J. Math. Imaging Vis. 34(3), 270–290 (2009)

  3. 3

    Jurie, F., Dhome, M.: Real time 3D template matching. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1, pp. 791–796 (2001)

  4. 4

    Hager G.D., Belhumeur P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Anal. Mach. Intell. 20(10), 1025–1039 (1998)

  5. 5

    Hager G.D., Dewan M., Stewart C.V.: Multiple kernel tracking with SSD. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogni. 1, 790–797 (2004)

  6. 6

    Collins R.T., Liu Y., Leordeanu M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)

  7. 7

    Zhou H., Yuan Y., Shi C.: Object tracking using SIFT features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)

  8. 8

    Cootes T.F., Edwards G.J., Taylor C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

  9. 9

    Jepson A.D., Fleet D.J., El-Maraghi T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296–1311 (2003)

  10. 10

    Gross R., Matthews I., Bakera S.: Active appearance models with occlusion. Image Vis. Comput. 24(6), 593–604 (2006)

  11. 11

    Lee S.H., Howlett R.J., Walters S.D.: Small engine control by fuzzy logic. J. Intell. Fuzzy Syst. 15, 207–217 (2004)

  12. 12

    Anderson, D., Keller, J.M., Skubic, M., Chen, X., He, Z. In: Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Recognizing falls from silhouettes, EMBS ’06, pp. 6388–6391 (2006)

  13. 13

    Chen, X., He, Z., Anderson, D., Keller, J.M., Skubic, M. In: Image Processing, 2006 IEEE International Conference on Adaptive silouette extraction and human tracking in complex and dynamic environments, pp. 561–564 (2006)

  14. 14

    Lowe, D.G. In: Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on Object recognition from local scale-invariant features, vol. 2, pp. 1150–1157 (1999)

  15. 15

    Kadir T., Brady M.: Saliency, scale and image description. Int. J. Comput. Vis. 45(2), 83–105 (2004)

  16. 16

    Harrism, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1988)

  17. 17

    Mikolajczyk K., Schmid C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

  18. 18

    Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

  19. 19

    Bay H., Ess A., Tuytelaars T., Van Gool L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

  20. 20

    Zitova B., Flusser J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

  21. 21

    Yilmaz A., Javed O., Shah M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)

  22. 22

    Hathaway R.J., Bezdek J.C., Hu Y.: Generalized fuzzy C-means clustering strategies using Lp norm distances. IEEE Trans. Fuzzy Syst. 8(5), 576–582 (2000)

  23. 23

    Lewis, J.P.: Fast template matching. In: Proceedings of the Vision Interface 95, pp. 120–123 (1995)

  24. 24

    Viola P., Jones M.: Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2002)

  25. 25

    The CAVIAR Project website http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  26. 26

    McCane B., Novins K., Crannitch D., Galvin B.: On benchmarking optical flow. Comput. Vis. Image Underst. 84, 126–143 (2001)

Download references

Author information

Correspondence to Raul E. Sanchez-Yanez.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ramirez-Paredes, J., Sanchez-Yanez, R.E. & Ayala-Ramirez, V. A fuzzy inference approach to template-based visual tracking. Machine Vision and Applications 23, 427–439 (2012). https://doi.org/10.1007/s00138-010-0314-8

Download citation

Keywords

  • Target tracking
  • Deformable template
  • Fuzzy system
  • Real-time vision