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Reseach Article

Intelligent Video Surveillance System based on Wavelet Transform and Support Vector Machine

by Meghana M. Deshpande, Jaideep G. Rana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 14
Year of Publication: 2012
Authors: Meghana M. Deshpande, Jaideep G. Rana
10.5120/7419-0453

Meghana M. Deshpande, Jaideep G. Rana . Intelligent Video Surveillance System based on Wavelet Transform and Support Vector Machine. International Journal of Computer Applications. 48, 14 ( June 2012), 42-45. DOI=10.5120/7419-0453

@article{ 10.5120/7419-0453,
author = { Meghana M. Deshpande, Jaideep G. Rana },
title = { Intelligent Video Surveillance System based on Wavelet Transform and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 14 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number14/7419-0453/ },
doi = { 10.5120/7419-0453 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:05.098316+05:30
%A Meghana M. Deshpande
%A Jaideep G. Rana
%T Intelligent Video Surveillance System based on Wavelet Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 14
%P 42-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic human detection is an important application where security is the main concern. As human detection problem involves classification of two objects as humans and others, a human detection using intelligent video surveillance system is presented using support vector machine to detect human in surveillance field. In this paper, in order to improve the efficiency of the machine learning 2D Wavelet transform based features are used. It consists of wavelet statistical features and wavelet co-occurrence features which are obtained from red, green and blue layers of sample images. The sample images are obtained through the video which forms training input to SVM. The experimental results demonstrate that the proposed system achieves good success rate for wavelet co-occurrence features.

References
  1. Ye, B. and Y, Wen. 2007 Gait Recognition based on DWT and SVM, International Conference on Wavelet analysis and Pattern recognition, 1382-1387.
  2. Xinnan, F. Lizhong, X. Xuewu, Z. and Lei, C. 2008 The Research and Application of Human Detection Based on Support Vector Machine using in Intelligent Video Surveillance System, Forth International Conference on Natural Computation,139-143.
  3. Mostayed, A. Mazumder, M. Kim, S. and Park, S. 2008 Abnormal Gait Detection Using Discrete Wavelet Transform in Domain, 4th Kuala Lumpur International Conference On Biomedical Engineering,383-386.
  4. Sudha, L. and Bhavani, R. 2011 SVM based biometric authorization system by video analysis of human gait, International Conference on Electronics Computer Technology, 301-304.
  5. El-Yacoubi, M. Shaiek, A. and Dorizzi, B. 2011 HMM-based gait modeling and recognition under different walking scenarios, International Conference of Multimedia Computing and Systems,1-5.
  6. Li, P. 2011 Gait Recognition via Fused Hidden Markov Models, Multimedia Information Networking and Security, 187-190.
  7. Libin, D. and Wenxin, S. 2011 An Algorithm of Gait Recognition Based on Support Vector Machine, Journal of Computational Information Systems, vol. 13, 4710-4715 .
  8. Mukane, M. Gengaje, S. and Bormane, S. 2011On Scale Invariance Texture Image Retrival using Fuzzy Logic and Wavelet Co-occurrence based Features, International journal of computer applications, Vol. 18(3),10-17.
  9. Pawar, p. and Jung, s. 2007 Support Vector Machine based Online Composite Helicopter Rotor Blade Damage Detection System, Journal Of Intelligent Material Systems And Structures, vol. 19, 1217-1228.
  10. Cortes, C. and Vapnik, V. 1995 Support Vector Networks, Machine Learning, 273-297.
  11. Vapnik, V. 1995 The Nature of Statistical Learning Theory, Springer-Verlag, New York.
  12. Boser, B. Guyon, I. and Vapnik, V. 1992 A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual workshop on Computational learning Theory Pittsburgh Pennsyl vania USA, 144-152.
Index Terms

Computer Science
Information Sciences

Keywords

Human Detection Support Vector Machine Video Surveillance Wavelet Transform