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Automated Vehicle Identification System based on Discrete Curvelet Transform for Visual Surveillance and Traffic Monitoring System

by N. G. Chitaliya, A. I. Trivedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 1
Year of Publication: 2012
Authors: N. G. Chitaliya, A. I. Trivedi
10.5120/9080-2471

N. G. Chitaliya, A. I. Trivedi . Automated Vehicle Identification System based on Discrete Curvelet Transform for Visual Surveillance and Traffic Monitoring System. International Journal of Computer Applications. 57, 1 ( November 2012), 39-44. DOI=10.5120/9080-2471

@article{ 10.5120/9080-2471,
author = { N. G. Chitaliya, A. I. Trivedi },
title = { Automated Vehicle Identification System based on Discrete Curvelet Transform for Visual Surveillance and Traffic Monitoring System },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 1 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number1/9080-2471/ },
doi = { 10.5120/9080-2471 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:21.364447+05:30
%A N. G. Chitaliya
%A A. I. Trivedi
%T Automated Vehicle Identification System based on Discrete Curvelet Transform for Visual Surveillance and Traffic Monitoring System
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 1
%P 39-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For identification of vehicles, Classifier is designed. Designing of vehicle classifier using the discrete Curvelet transform via wrapping is proposed in this paper. To increase the efficiency of classifier, 3 class structures designed with respect to the ratio of length and width of the vehicle or person on the road. Each Image is preprocessed with Unsharp filtering which provides edge details. Each sharpen Image is converted into binary image by applying global threshold using otsu's method . Binary images are decomposed using fast discrete Curvelet transform. The Curvelet coefficients from low frequency and high frequency component at different scale and orientations are obtained. The frequency coefficients used to create the feature vector matrix for all images. The Eigen value of the feature matrix is used for dimensionality reduction. The Experiments carried out on different types of vehicle images. The results of the classifier show the efficiency to handle the real time dataset.

References
  1. S. Gupte. O. Masoud, N. P. Paparnkolopoulos, Detection and classification of Vehicle, IEEE Trans. On Intelligent Transportaion Systems, Vol. 3(1) (2002), pp. 33-47.
  2. M. Kirby and L. Sirovich, Application of the Karhunen-Loeve procedure for the characterization of human faces, IEEE Transaction on pattern analysis and Machine Intelligence. 12(1) (1990), pp. 103-108.
  3. M. Turk and A. Pentland. Eigenfaces for recognition, Journal of Cognitive Neuro Science, 3(1) (1991), pp. 71-86.
  4. Tuan HuThi, Kostia Robert, Sijun Lu and Jian Zhang, Vehicle classification at night time using Eigenspaces and Support Vector Machine, IEEE International Congress on Image and Signal Processing (CISP 2008), China, May 2008.
  5. Ch. Srinivasa Rao, S. Srinivas Kumar, B. N. Chatterji ,Content Based Image retrieval using Contourlet Transform, ICGST-GVIP Journal, volume 7(3), (2007) ,pp.
  6. Zehang, G. Bebis, and R. Miller, On-road vehicle detection using evolutionary Gabor filter optimization, IEEE Transactions on Intelligent Transportation systems, vol6 (2) (2005), pp. 125-137.
  7. Harkirat S. Sahambi and K. Khorasani , A Neural network appearance based 3-D object recognition using Independent component analysis,IEEE Transaction on Neural Network, vol. 14(1)(2003)
  8. Xuebin Xu, Deyun Zhang, Xinman Zhan Zhang,"An efficient method for human face recognition using nonsubsampled Contourlet transform and support vector machine, Optica Applicata, Vol. (3)(2009) pp 601-615.
  9. Starack J. L. ,Candes E. J. , Donoho D. L. , The Curvelet transform for image denoising, IEEE Transactions on Image Processing ,vol. 11(6)(2002), pp. 670–684.
  10. Tanaya Mandal, Angshul Majmudar, Q. M. Jonathan W U, Face recognition by Curvelet based feature extraction, International Conference on Intelligent Automation and Robotics, LNCS 4633, pp. 806–817, 2007
  11. DO M. N. , Vetterli M. , the Contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14(12), 2005, pp. 2091 –2106.
  12. Zhou J. , Cunha A. L. , M. N. Do. , Nonsubsampled Contourlet transform: construction and application in enhancement, International Conference on Image Processing, ICIP, Vol. 1, pp,469 –472,2005
  13. Yang L. , Guo B. L. , NI W. , Multimodality medical image fusion based on multiscale geometric analysis of Contourlet transform, Neurocomputing 72(13), 2008, pp. 203– 211.
  14. LU Y. , Do M. N. , A new Contourlet transform with sharp frequency localization", IEEE International Conference on Image Processing, pp. 1629– 1632,2006.
  15. Hanglong YU, Shengsheng YU et al. , An image compression scheme based on modified Contourlet transform, Computer Engineering and Application 41(1), 2005, pp. 40– 43.
  16. Jun Yan, Muraleedharan R. , Xiang YE, Osadciw L. A. , Contourlet based image compression for wireless communication in face recognition system, IEEE International Conference on Communication, pp. 505–509,2008.
  17. Bin Yang, Shutao Li, Fengmei Sun, Image fusion using nonsubsampled Contourlet transform, Proceedings of the 4th International Conference on Image and Graphics, ICIG, pp. 719–724, 2007.
  18. Hedieh SajediI, Mansour Jamzad, A based-based face detection method in color images, International Conference on Signal Image Technologies and Internet Based Systems, SITIS pp. 727– 732,2007
  19. N. G. Chitaliya , A. I. Trivedi, Feature Extraction using Wavelet-PCA and Neural network for application of Object Classification & Face Recognition," International Conference on Computer Engineering and Application, ICCEA, Vol 1,pp. 510-514,2010
  20. E. J. Candes, L. Demanet, D. L. Donoho, and L. Ying, Fast Discrete Curvelet Transforms, Multiscale Modelling and Simulation, vol. 5, pp. 861-899, 2005.
  21. David L. Donoho & Mark R. Duncan, Digital Curvelet Transform,Strategy, implementation and Experiments, Stanford University, November, 1999
  22. J. L. Starck, E. Candes, and D. L. Donoho, The Curvelet Transform for Image Denoising, IEEE Transactions on Image Processing, 11(6), 2002, 670 -684.
  23. N. G. Chitaliya and Prof. A. I. Trivedi. An Efficient Method for Face Feature Extraction and Recognition based on Contourlet Transform and Principal Component Analysis using Neural Network, International Journal of Computer Applications 6(4),2010,pp. 28–34
  24. N. G. Chitaliya and Prof. A. I. Trivedi. An Efficient Method for Face Feature Extraction and Recognition based on Contourlet Transform and Principal Component Analysis, Proceedings of the International Conference and Exhibition on Biometrics Technology, 2010,pp. 52-61
  25. VOC 2006 dataset – http:// pascallin. ecs. soton. ac. uk/challenges/VOC/voc2006/.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Curvelet Transform Vehicle Classifier Euclidean Distance Principal Component Analysis Feature Extraction Neural Network

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