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Reseach Article

Companied Edge Flow Histogram and Color Histogram to Represent Tracking Objects

by Sallama Resen, Hala Bahjat
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 14
Year of Publication: 2014
Authors: Sallama Resen, Hala Bahjat
10.5120/15280-4045

Sallama Resen, Hala Bahjat . Companied Edge Flow Histogram and Color Histogram to Represent Tracking Objects. International Journal of Computer Applications. 87, 14 ( February 2014), 38-42. DOI=10.5120/15280-4045

@article{ 10.5120/15280-4045,
author = { Sallama Resen, Hala Bahjat },
title = { Companied Edge Flow Histogram and Color Histogram to Represent Tracking Objects },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 14 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number14/15280-4045/ },
doi = { 10.5120/15280-4045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:57.610739+05:30
%A Sallama Resen
%A Hala Bahjat
%T Companied Edge Flow Histogram and Color Histogram to Represent Tracking Objects
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 14
%P 38-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tracking moving objects from moving platform in videos sequence is a challenging task . object movement and platform movement are sources of variations in scene. Mean Shift Algorithm (MSA) is the common tracking algorithm due simple and efficient procedure. Correct Background Weight Histogram(CBWH)decrease background effect in target representation module . The main drawbacks in MSA is the ineffective model representation to handle illumination variation and occultation problems . MSA failed to track objects in video containing wide ranges of variations and background motion. In this paper motion information is exploited from edge flow by gradient differential. Histogram for edge flow combined with color histogram called Motion Flow Histograms (MFH). MFH used to represent tracking target between two successive frames. New module target representation reduces the false tracker rate without evident increasing time compare to the classical tracking MSA and CBWH.

References
  1. B. Karasulu and S. Korukoglu, "Performance evaluation software moving object detection and tracking in videos", SpringerBriefs 7 in Computer Science, DOI: 10. 1007/978-1-4614-6534-8_2013,
  2. N. Magoch T. & M. M. Kuber, "A Comparative analysis of kernel-based target tracking methods using different colour feature based target models",Department of Computer Engineering, Defence Institute of Advanced Technology, Pune, India, International Journal of Computer Science and Informatics ISSN (PRINT): 2231 –5292, Vol-1, Iss-4, 2012.
  3. S Ili?,Technischen,"Tracking and detection in computer vision Feature descriptors", University München Winter Semester 2009/2010.
  4. A. Yilmaz ,"Object Tracking A Survey", Ohio State University,Omar Javed,ObjectVideo, Inc. and Mubarak Shah,University of Central Florida , ACM Computing Surveys, Vol. 38, No. 4, Article 13, Publication date: December 2006.
  5. M. Proesmans, L. Van Gool, E. Pauwels, and A. Oosterlinck. "Determination of optical flow and its discontinuities using non-linear diffusion". In Proceedings of the 3rd European,Conference on Computer Vision, Stockholm, Sweden, volume 2, pages 295–304, 1994.
  6. D. , Huang, Q. , Jiang, S. , Yao, H. , Gao, W. " Mean-shift blob tracking with adaptive feature selection and scale adaptation". In International Conference Image Processing (2007).
  7. Q. Luo • X. Kong • G. Zeng, "Human action detection via boosted local motion histograms", Jianping Fan Received: 3 December 2007 , Accepted: 8 September 2008 © Springer-Verlag 2008.
  8. A. Yilmaz, M. Shah," Recognizing human actions in videos acquired by uncalibrated moving cameras". IEEE Int. Conf. Computer. Vis. 1, 150–157 (2005)
  9. S. Kahlouche, O. Djekoune, D. Djebrouni, D. Meriche ,"Segmentation by motion based on optical flow histogram", Centre de Développement des Technologies Avancées CDTA,Houch Oukil, BP 17, Baba Hassen – Alger – Algérie.
  10. C. Fennema and W. Thompson, "Velocity determination in scenes containing several moving objects", Computer Graphics and Image Processing,9 (1979), pp. 301–315.
  11. R. Paquin and E. Dubois, « A spatio-temporal gradient method for estimating the displacement field in time varying imagery », Comp. Vis. ,Graphics and Image Processing,Vol. 21, pp. 205-221,1983
  12. W. ,James ,R. Davis ,"Recognizing movement using motion histograms",MIT Media Laboratory, 20Ames Steet, Cambridge,MA Media Laboratory, Perceptual Computing Section Technical Report ,April.
  13. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings,of the Conference on Computer Vision and Pattern Recognition, San Diego, California,USA, pages 886–893, 2005
Index Terms

Computer Science
Information Sciences

Keywords

MSA Edge Flow Histogram Gradient Differentiation Histogram CBWH