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Multinational vehicle license plate detection in complex backgrounds.

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Title: Multinational vehicle license plate detection in complex backgrounds.
Authors: Asif, Muhammad Rizwan1,2, Chun, Qi1 qichun@mail.xjtu.edu.cn, Hussain, Sajid1,3, Fareed, Muhammad Sadiq1, Khan, Subhan2
Source: Journal of Visual Communication & Image Representation. Jul2017, Vol. 46, p176-186. 11p.
Subjects: Automobile license plates, Statistical methods in image analysis, Algorithms, Histograms, Digital image processing, Mathematics
Abstract: Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25 s on average that can achieve real time performance. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Visual Communication & Image Representation is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 123342791
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Multinational vehicle license plate detection in complex backgrounds.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Visual+Communication+%26+Image+Representation%22">Journal of Visual Communication & Image Representation</searchLink>. Jul2017, Vol. 46, p176-186. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Automobile+license+plates%22">Automobile license plates</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+methods+in+image+analysis%22">Statistical methods in image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Histograms%22">Histograms</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25 s on average that can achieve real time performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Visual Communication & Image Representation is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.jvcir.2017.03.020
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 176
    Subjects:
      – SubjectFull: Automobile license plates
        Type: general
      – SubjectFull: Statistical methods in image analysis
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Histograms
        Type: general
      – SubjectFull: Digital image processing
        Type: general
      – SubjectFull: Mathematics
        Type: general
    Titles:
      – TitleFull: Multinational vehicle license plate detection in complex backgrounds.
        Type: main
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          Name:
            NameFull: Asif, Muhammad Rizwan
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            NameFull: Chun, Qi
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            NameFull: Hussain, Sajid
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            NameFull: Fareed, Muhammad Sadiq
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            NameFull: Khan, Subhan
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            – D: 01
              M: 07
              Text: Jul2017
              Type: published
              Y: 2017
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              Value: 46
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            – TitleFull: Journal of Visual Communication & Image Representation
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