Advertisement

Local Binary Pattern as a Texture Feature Descriptor in Object Tracking Algorithm

  • Prajna Parimita Dash
  • Dipti Patra
  • Sudhansu Kumar Mishra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

In this paper, we address a real-time object tracking algorithm considering local binary pattern (LBP) as a feature descriptor. In addition to texture feature, Ohta color features are included in the feature vector of the covariance tracking algorithm. The performance of the proposed algorithm is compared with some other competitive object tracking algorithms such as the RGB feature-based covariance method and color histogram method. The comparisons of the performance among these algorithms include detection rate and computational time. These methods have been applied to four different challenging situations, and the resulting experimental results show the robustness of the proposed technique against occlusion, camera motion, appearance, and change in illumination condition.

Keywords

Object tracking Covariance matrix Local binary pattern Riemannian geometry 

References

  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4) Article 13 (2006)Google Scholar
  2. 2.
    Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithms. In: Proceedings of 19th International Conference on Pattern Recognition, pp. 1–4, ICPR (2008)Google Scholar
  3. 3.
    Patra, D., Kumar, S.K., Chakraborty, D.: Object tracking in video images using hybrid segmentation method and pattern matching. IEEE India Council Conference INDICON (2009)Google Scholar
  4. 4.
    Porikli, F.: Integral histogram: A fast way to extract histograms in Cartesian spaces. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition San Diego, vol. 1, pp. 829–836 (2005)Google Scholar
  5. 5.
    Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Proceedings of 9th European Conference on Computer Vision, Graz, vol. 2, pp. 589–600 (2006)Google Scholar
  6. 6.
    Porikli, F., Tuzel, O., Meer, P.: Covariance Tracking using model update based on means on Riemannian manifolds. Computer Vision and Pattern Recognition, New York (2006)Google Scholar
  7. 7.
    Ando, R., Ohki, H., Fujita, Y.: A comparison with covariance features on player uniform number recognition. In: frontiers Computer Vision (2011)Google Scholar
  8. 8.
    Dash, P.P, Aitha, S., Patra, D.: Ohta based covariance technique for tracking object in video scene. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) (2012)Google Scholar
  9. 9.
    Gotlieb, C.C., Kreyszig, H.E.: Texture descriptors based on co-occurrence matrices. Comput. Vis. Graph. Image Process. 51(1), 70–86 (1990)CrossRefGoogle Scholar
  10. 10.
    Pietik¨ainen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Patt. Recogn. 33(1), 43–52 (2000)Google Scholar
  11. 11.
    Ojala, T., Valkealahti, K., Oja, E., Pietikainen, M.: Texture discrimination with multi-dimensional distributions of signed gray level differences. Patt. Recogn. 34(3), 727–739 (2001)CrossRefMATHGoogle Scholar
  12. 12.
    Pietik¨ainen, M., Zhao, G.: Local binary pattern descriptors for dynamic texture recognition. In: Proceedings International Conference Patterns Recognition, pp. 211–214 (2006)Google Scholar
  13. 13.
    Ohta, Y.I., Kanade, T., Sakai, T.: Color information for region segmentation. Comput. Graph. Image Proc., 13, 222–241 (1980)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Prajna Parimita Dash
    • 1
  • Dipti Patra
    • 1
  • Sudhansu Kumar Mishra
    • 2
  1. 1.IPCV LabNational Institute of TechnologyRourkelaIndia
  2. 2.Birla Institute of TechnologyRanchiIndia

Personalised recommendations