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Muhammad Rizwan Asif

License plate detection for multi-national vehicles – a generalized approach

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  • Muhammad Rizwan Asif
  • Chun Qi, Xi'an Jiaotong University
  • ,
  • Tiexiang Wang, Xi'an Jiaotong University
  • ,
  • Muhammad Sadiq Fareed, Xi'an Jiaotong University
  • ,
  • Subhan Khan, University of New South Wales

License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles.

Original languageEnglish
JournalMultimedia Tools and Applications
Pages (from-to)35585-35606
Number of pages22
Publication statusPublished - Dec 2019

    Research areas

  • Intelligent vision system, License plate detection, Local image features, Multi-national vehicles, Vehicle identification, Vehicle rear lights

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ID: 174169613