Real time number plate recognition and tracking vehicle system

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 12
Year of Publication : 2015
Authors : Sagar Badgujar, Amol Mahalpure, Priyaka Satam, Dipalee Thakar, Prof. Swati jaiswal

How to Cite?

Sagar Badgujar, Amol Mahalpure, Priyaka Satam, Dipalee Thakar, Prof. Swati jaiswal, "Real time number plate recognition and tracking vehicle system," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 12, pp. 5-8, 2015. Crossref,


 Recently we used the GPS technology to find the location of objects. In this project we improve the GPS technology using various algorithms. A camera based algorithm for real-time robust number plate detection and recognition was proposed, and especially designed for autonomous vehicles. The image processing of can be divided into three steps, including preprocessing, detection and recognition. Firstly Vehicle information registration module involves- main content of pre-processing. Such as new registration, Username, Vehicle Number Plate, Image of Vehicle and Related information about vehicle. In detection step, Video and Image as the prior knowledge is performed to scan the scene in order to quickly identify the number plate. Using image processing characters has been analysed as position of pixels. For recognition, Optical character Recognition (OCR) Algorithm is used. By using these three steps number plate should be recognize. Global Positioning System (GPS) tool kit analyse the speed, working of engine and SMS service has been provided. Project presents a novel vehicle speed measurement method, which contains the improved three frame deference algorithm and the proposed grey constraint optical algorithm. The contour of moving vehicles can be detected exactly. Through the proposed grey constraint optical algorithm, the vehicle contour as optical own value, which is the speed (pixels/s) of the vehicle in the image, can be computed accurately. Then, the velocity (km/h) of the vehicles is calculated by the optical flow value of the vehicles contour and the corresponding ratio of the image pixels to the width of the road experimental comparisons between the method and other VSM methods show that the proposed approach has a satisfactory estimate of vehicle speed.




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