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Reseach Article

Use of Multiple Thresholding Techniques for Moving Object Detection and Tracking

by S. Vijayalakshmi, D. Christopher Durairaj
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 1
Year of Publication: 2013
Authors: S. Vijayalakshmi, D. Christopher Durairaj
10.5120/13822-0809

S. Vijayalakshmi, D. Christopher Durairaj . Use of Multiple Thresholding Techniques for Moving Object Detection and Tracking. International Journal of Computer Applications. 80, 1 ( October 2013), 1-7. DOI=10.5120/13822-0809

@article{ 10.5120/13822-0809,
author = { S. Vijayalakshmi, D. Christopher Durairaj },
title = { Use of Multiple Thresholding Techniques for Moving Object Detection and Tracking },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 1 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number1/13822-0809/ },
doi = { 10.5120/13822-0809 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:23.063411+05:30
%A S. Vijayalakshmi
%A D. Christopher Durairaj
%T Use of Multiple Thresholding Techniques for Moving Object Detection and Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 1
%P 1-7
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present work proposes many threshold techniques for moving object detection and tracking system. It applies more than one threshold techniques during segmentation phase of the work. Object detection is done by background subtraction with Alpha method and object tracking is carried out by feature point tracking approach. It is observed that Otsu threshold method seems to have produced a perfect extraction and yielded good result in moving object tracking. The results of applying multiple thresholds are reported in this paper.

References
  1. R1. Joshi, Kinjal A. , and Darshak G. Thakore, 2012. "A Survey on Moving Object Detection and Tracking in Video Surveillance System, "International Journal of Soft Computing and Engineering (IJSCE) ISSN", 2231-2307.
  2. Dheeraj Agrawal, Nitin Meena, 2013. "Performance Comparison of Moving Object Detection Techniques in Video Surveillance System" The International Journal of Engineering And Science (IJES), Volume. 2, Issue. 01, 240-242.
  3. Vision System for Relative Motion Estimation from Optical Flow: Sergey M. Sokolov, Andrey A. Boguslavsky, Felix A. KuftinKeldysh Institute for Applied Mathematics RAS Moscow, Russia. 2004.
  4. M. Yachida, M. Asada and S. Tsuji, 1981. "Automatic analysis of moving images", IEEE Trans. Pattern Anal. Mach. Intel. , vol. PAMI-3, 12 -19.
  5. Visser, R. , Sebe, N. , & Bakker, 2002. Object recognition for video retrieval. In Image and Video Retrieval, Springer Berlin Heidelberg, 262-270.
  6. Häusler, G. , and D. Ritter. 1999. "Feature-based object recognition and localization in 3D-space, using a single video image. " Computer Vision and Image Understanding 73. 1, 64-81.
  7. I. K. Sethi and R. Jain, 1987. "Finding trajectories of feature points in a monocular image sequence", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, 56 -73.
  8. C. Stauffer and W. E. L. Grimson, 1999. "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Computer Vision and Pattern Recognition.
  9. Elgammal, A. , Dura swami, R. , Harwood, D. , Davis, L, 2002. Background and foreground modeling using nonparametric kernel density for visual surveillance. Proceedings of the IEEE 90, 1151-1163.
  10. T. Bouwmans, F. El Baf, and V. B, 2010. Statistical Background Modeling for Foreground Detection: A Survey, volume 4 of Handbook of Pattern Recognition and Computer Vision, chapter 3. World Scientific Publishing.
  11. Sebastian Brutzer, Benjamin H¨oferlin, 2011. "Evaluation of Background Subtraction Techniques for Video Surveillance" Gunther Heidemann Intelligent Systems Group, Universit¨at Stuttgart, Germany.
  12. J. Song, R. L. Stevenson, and E. J. Delp, 1989. "The Use of Mathematical Morphology in Image Enhancement", Proceedings of the 32nd Midwest Symposium on Circuits and Systems, Urbana-Champaign, IL, 67-70.
  13. Fathy, M. , and Siyal, M. Y. , 1995. "An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis", Pattern Recognition Letters, 1321-1330.
  14. Sushil Kumar, et al. , AMEA, in 2012. "2D Maximum Entropy Method for Image Thresholding Converge with Differential Evolution" Advances in Mechanical Engineering and its Applications (AMEA) Vol. 2, No. 3, 189-192.
  15. Jeon, J. , and Manmatha, 2004. "Using maximum entropy for automatic image annotation" Springer link, Lecture Notes in Computer Science The, Vol. 3115/2004.
  16. J. Z. Liu and W. Q. Li, 1993. "The automatic thresholding of gray-level pictures via two-dimensional Otsu method," IEEE Int. Computer Vision and Pattern Recognition.
Index Terms

Computer Science
Information Sciences

Keywords

Object detection tracking threshold methods