Advertisement

A Robust Tracking Algorithm Based on HOGs Descriptor

  • Daniel Miramontes-Jaramillo
  • Vitaly Kober
  • Víctor Hugo Díaz-Ram
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

A novel tracking algorithm based on matching of filtered histograms of oriented gradients (HOGs) computed in circular sliding windows is proposed. The algorithm is robust to geometrical distortions of a target as well as invariant to illumination changes in scene frames. The proposed algorithm is composed by the following steps: first, a fragment of interest is extracted from a current frame around predicted coordinates of the target location; second, the fragment is preprocessed to correct illumination changes; third, a geometric structure consisting of disks to describe the target is constructed; finally, filtered histograms of oriented gradients computed over geometric structures of the fragment and template are matched. The performance of the proposed algorithm is compared with that of similar state-of-the-art techniques for target tracking in terms of objective metrics.

Keywords

Video Sequence Tracking Algorithm Target Tracking Current Frame Oriented Gradient 
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.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computer Surveys 38(4), 45 p. (2006)CrossRefGoogle Scholar
  2. 2.
    Sethi, I., Jain, R.: Finding trayectories of feature points in a molecular image secuence. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(1), 56–73 (1987)CrossRefGoogle Scholar
  3. 3.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. Int. Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  4. 4.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  5. 5.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-Learning-Detection. IEEE Trans. Pattern Anal. 34(7), 1409–1424 (2012)CrossRefGoogle Scholar
  6. 6.
    Talu, F., Turkoglu, I., Cebeci, M.: A hybrid tracking method for scaled and oriented objects in crowded scenes. Expert Systems with Applications 38, 13682–13687 (2011)Google Scholar
  7. 7.
    Nejhum, S., Ho, J., Yang, M.H.: Online visual tracking with histograms and articulating blocks. Computer Vision and Image Understanding, 901–914 (2010)Google Scholar
  8. 8.
    Díaz-Ramírez, V.H., Kober, V.: Target recognition under nonuniform illumination conditions. Appl. Opt. 48, 1408–1418 (2009)CrossRefGoogle Scholar
  9. 9.
    Martínez-Díaz, S., Kober, V.: Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition. Opt. Eng. 47(6) (2008)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  11. 11.
    Miramontes-Jaramillo, D., Kober, V., Díaz-Ramírez, V.H.: CWMA: Circular Window Matching Algorithm. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 439–446. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Rong Li, X., Jilkov, V.P.: Survey of maneuvering target tracking. Part I. dynamic models. IEEE Trans. on Aerosp. Electron. Sys. 39(4), 1333–1364 (2003)CrossRefGoogle Scholar
  13. 13.
    Díaz-Ramírez, V.H., Picos, K., Kober, V.: Target tracking in nonuniform illumination conditions using locally adaptive correlation filters. Opt. Comm. 323, 32–43 (2014)CrossRefGoogle Scholar
  14. 14.
    Pratt, W.K.: Digital Image Processing. John Wiley & Sons (2007)Google Scholar
  15. 15.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behavior. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 34(3), 334–352 (2004)CrossRefGoogle Scholar
  16. 16.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Computer Vision 61(1), 103–112 (2005), http://staff.science.uva.nl/~aloi/ CrossRefGoogle Scholar
  17. 17.
    Zhou, H., Yuan, Y., Shi, C.: Object Tracking Using SIFT Features and Mean Shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)CrossRefGoogle Scholar
  18. 18.
    Zhou, D., Hu, D.: A robust object tracking algorithm based on SURF. In: Int. Conf. on Wireless Comm. Sign. Proc., pp. 1–5 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Miramontes-Jaramillo
    • 1
  • Vitaly Kober
    • 1
    • 2
  • Víctor Hugo Díaz-Ram
    • 3
  1. 1.CICESEEnsenadaMéxico
  2. 2.Department of MathematicsChelyabinsk State UniversityRussian Federation
  3. 3.CITEDI-IPNTijuanaMéxico

Personalised recommendations