Abstract
There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1-norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost.
Highlights
Automatic license plate recognition (ALPR) has become increasingly popular in intelligent transportation systems
In order to study the accuracy of the SB-ALPR algorithm, since this method is based on still-image, we applied it to 660 various quality images
Looking at the evolution of ALPR systems over decades, there are two pronounced problems currently associated with these methods; the execution time associated with exhaustive search for large high-resolution images and the accuracy of ALPR due to various technical and surrounding problems
Summary
Automatic license plate recognition (ALPR) has become increasingly popular in intelligent transportation systems. Most of the available localization methods, which have to deal with large-size images in real-time scenarios, become computationally intensive when cars are moving and very time-consuming. Developing more adequate algorithms for localization of car number plates for faster processing of such large images often becomes necessary. Many researchers regard localization as the most important step of ALPR. Several methods have been developed for localizing the number plate on a car, to name a few: edge detection using Gabor and wavelet transforms, color properties of the number plates [1], discrete Fourier transform (DFT) [2], and analyzing the histogram [3]. The car number plates are localized through the application of one or more of the above methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
