CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach

by Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kr. Basu
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 23
Year of Publication: 2010
Authors: Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kr. Basu
10.5120/542-706

Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kr. Basu . Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach. International Journal of Computer Applications. 1, 23 ( February 2010), 25-31. DOI=10.5120/542-706

@article{ 10.5120/542-706,
author = { Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kr. Basu },
title = { Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 23 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number23/542-706/ },
doi = { 10.5120/542-706 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:48:41.337949+05:30
%A Satadal Saha
%A Subhadip Basu
%A Mita Nasipuri
%A Dipak Kr. Basu
%T Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 23
%P 25-31
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traffic monitoring system has now become an essential administrative part in most of the developed and developing countries. In general, such systems monitor/identify the vehicles exceeding speed limits, or monitor the vehicles crossing the stop line at red traffic signal. It may also be used for registering the vehicles getting entry in a shopping mall or in a railway station or in an airport. The key modules of these monitoring systems are: (i) localization of license plates within the image and (ii) recognizing the license number using an OCR system. The present work addresses the first module of the system. The color information of the license plate is used as the knowledge base for training an artificial neural network system using back propagation algorithm. The trained network is then used to find the potential license plate region within a new traffic image. The scheme is applied in a real life outdoor environment at some road crossings in an Indian city. The result is found to be quite satisfactory giving an accuracy of around 80%.

References
  1. O. Martinsky, “Algorithmic and Mathematical Principles of Automatic Number Plate Recognition System”, B. Sc. Thesis, BRNO University of Technology, 2007.
  2. Erik Bergenudd, “Low-Cost Real-Time License Plate Recognision for a Vehicle PC”, Master’s Degree Project, KTH Electrical Engineering, Sweden, December 2006.
  3. J. R. Parker and P. Federl, “An Approach to License Plate Recognition”, Computer Science Technical Report (1996-591-1. I), 1996.
  4. H. Kawasnicka and B. Wawrzyniak, “License Plate Localization and Recognition in Camera Pictures”, AI-METH 2002, Poland, November 2002.
  5. H. Mahini, S. Kasaei, F. Dorri and F. Dorri, “An Efficient Features-Based License Plate Localization Method”, Proceedings of 18th International Conference on Pattern Recognition, 2006.
  6. W. Jia, H. Zhang, X. He and M. Piccardi, “Mean Shift for Accurate License Plate Localization”, Proceedings of 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, Sept. 2005.
  7. Cesar Garcia-Osorio, Jose-Francsico Diez-Pastor, J. J. Rodriguez, J. Maudes, “License Plate Number Recognition New Heuristics and a comparative study of classifier”, cibrg.org/documents/Garcia08ICINCO.pdf.
  8. C. N. Anagnostopoulos, I. Anagnostopoulos, V. Loumos and E. Kayafas, “A license plate recognition algorithm for Intelligent Transport applications”, www.aegean.gr/culturaltec/canagnostopoulos/cv/T-ITS-05-08-0095.pdf.
  9. S. Draghichi, “A neural network based artificial vision system for license plate recognition”, International Journal for Neural Systems, Vol. 8, 1997, pp. 113-126.
  10. V. S. L. Nathan, Ramkumar. J, Kamakshi. P. S, “New approaches for license plate recognition system”, ICISIP 2004, p.p. 149-152.
  11. Satadal Saha, Subhadip Basu, Mita Nasipuri and Dipak Kumar Basu, “An Offline Technique for Localization of License Plates for Indian Commercial Vehicles”, Proceedings of IEEE National Conference on Computing and Communication Systems (COCOSYS-09), UIT, Burdwan, January 02-04, 2009, pp. 206-211.
  12. Satadal Saha, Subhadip Basu, Mita Nasipuri and Dipak Kumar Basu, “License Plate localization from vehicle images: An edge based multi-stage approach”, In press International Journal on Recent Trends in Engineering (Computer Science), Vol. 1, No. 1, 2009, pp. 284-288.
  13. Satadal Saha, Subhadip Basu, Mita Nasipuri and Dipak Kumar Basu, “Development of an Automated Red Light Violation Detection System (RLVDS) for Indian vehicles”, Proceedings of IEEE National Conference on Computing and Communication Systems (COCOSYS-09), UIT, Burdwan, January 02-04, 2009, pp. 59-64.
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Second Edition, Pearson Education Asia, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

RGB-HSI Back propagation algorithm connected component labeling Sobel edge detector Hough transform

Powered by PhDFocusTM