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Reseach Article

Objects Tracking in Images Sequence using Local Binary Pattern (LBP)

by H. Rami, M. Hamri, Lh. Masmoudi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 20
Year of Publication: 2013
Authors: H. Rami, M. Hamri, Lh. Masmoudi
10.5120/10582-5288

H. Rami, M. Hamri, Lh. Masmoudi . Objects Tracking in Images Sequence using Local Binary Pattern (LBP). International Journal of Computer Applications. 63, 20 ( February 2013), 19-23. DOI=10.5120/10582-5288

@article{ 10.5120/10582-5288,
author = { H. Rami, M. Hamri, Lh. Masmoudi },
title = { Objects Tracking in Images Sequence using Local Binary Pattern (LBP) },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 20 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number20/10582-5288/ },
doi = { 10.5120/10582-5288 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:52.142483+05:30
%A H. Rami
%A M. Hamri
%A Lh. Masmoudi
%T Objects Tracking in Images Sequence using Local Binary Pattern (LBP)
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 20
%P 19-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present a method for objects tracking in images sequence. This approach is achieved into two main steps. In the first one, we constructed the Local Binary Pattern (LBP) histogram pattern of each image in the sequence and the reference pattern. In the second one, we perform the algorithm by the pattern selected based on a distance measures to find similarity between two histograms. The maximum LBP histogram distance gives best results than the chi-square one. The proposed approach has been tested on synthetic and real sequence images and the results are satisfactory.

References
  1. Ghosh and Webb. 1994. Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules. Biophys. J. 66:1301–1318.
  2. Lee, and al. 1991. Direct observation of Brownian motion of lipids in a membrane. Proc. Nat. Acad. Sci. U. S. A. 88:6274 – 6278.
  3. Anderson, and al. 1992. Tracking of cell surface receptors by fluorescence digital imaging microscopy using a charge-coupled device cam-era. J. Cell Sci. 101:415– 425. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  4. Smith, and al. 1999. A direct comparison of selectin-mediated transient, adhesive events using high temporal resolution. Biophys. J. 77:3371–3383.
  5. Gelles, and al. 1988. Tracking kinesin-driven movements with nanometre-scale precision. Nature. 331:450 – 453.
  6. Kusumi, and al. 1993. Confined later diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured endothelial cells. Biophys. J. 65:2021–2040.
  7. ] Guilford and Gore, 1995. The mechanics of arteriole interstitium interaction. Microvas. Res. 50:260 –287.
  8. Vanne, and al, 2006. "A High-Performance Sum of Absolute Difference Implementation for Motion Estimation," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 16, no. 7, pp. 876-883.
  9. Kanade and Okutomi. 1994. A stereo matching algorithm with an adaptive window: theory and experiment. IEEE Transactions for Pattern Analysis and Machine Intelligence 16, 920-932.
  10. Ojala and al, 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987
  11. Cho, and Yun, 2005 . Selective-attention correlation measure for precision video tracking. IEICE Trans. Inf. Syst. E88-D(5), 1041–1049
  12. Bohs, and al. 1999. Speckle tracking for multi-dimensional ?ow estimation. Ultrasonics 38, 369–375 (2000)
  13. Heikkil and Pietik ,2006. A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:657–662
  14. Timo Ahonen and al, 2009. Rotation invariant image description with local binary pattern histogram
  15. Ojala T, Pietikäinen M & Harwood D (1996) A comparative study of texture measures with classification based on featured distribution. Pattern Recognition, 29(1):51-59.
  16. A. Hadid, M. Pietikainen and T. Ahonen. A Discriminative Feature Space for Detecting and Recognizing Faces. Proc of CVPR 2004.
  17. Jo Chang-yeon, "Face Detection using LBP features," Final Project Report, December 2008.
  18. Olivier STRAUSS Laboratoire d'Informatique, de Robotique et de Micro-électronique de Montpellier
  19. Département Robotique LIRMM 161, Rue ADA 34392 Montpellier CEDEX 5 France
  20. Hafner, J. , Sawhney, H. , Equitz, W. , Flickner, M. , Niblack, W. : Efficient color histogram indexing for quadratic form distance functions. PAMI (1995)
  21. Rubner, Y. , Tomasi, C. , Guibas, L. J. : The earth mover's distance as a metric for image retrieval. IJCV (2000)
  22. Snedecor, G. , Cochran, W. : Statistical Methods, ed 6. Ames, Iowa (1967)
  23. Cula, O. , Dana, K. : 3D texture recognition using bidirectional feature histograms. IJCV (2004)
  24. Zhang, J. , Marszalek, M. , Lazebnik, S. , Schmid, C. : Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV (2007) 3
  25. Varma,M. , Zisserman, A. : A statistical approach to material classification using image patch exemplars. PAMI (2009) 3
  26. Xu, D. , Cham, T. , Yan, S. , Duan, L. , Chang, S. : Near Duplicate Identification with Spatially Aligned Pyramid Matching. CSVT (accepted) 3
  27. Forss´en, P. , Lowe, D. : Shape Descriptors for Maximally Stable Extremal Regions. In: ICCV. (2007) 3
  28. Belongie, S. , Malik, J. , Puzicha, J. : Shape matching and object recognition using shape contexts. PAMI (2002) 3, 11
  29. Ling, H. , Jacobs, D. : Shape classification using the inner-distance. PAMI (2007) 3, 11
  30. Martin, D. , Fowlkes, C. , Malik, J. : Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI (2004).
  31. Mehmet Emre Sargin and al. 2005. Combined Gesture-Speech Analysis and Synthesis eNTERFACE05 Workshop in Mons, Belgium.
Index Terms

Computer Science
Information Sciences

Keywords

Sequence image Computer vision Tracking LBP histogram chi-square distance