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Multiple Vehicle License Plate Location in Complex Background

Authors
  • Zhao, Yaxin1
  • Zhao, Li2
  • Li, Ya3
  • 1 School of Computer Science and Engineering Technological University, Xi’an , (China)
  • 2 School of Computer Science and Engineering, Xi’an Technological University, Xi’an , (China)
  • 3 Chong Qing Water Resources And Electric Engineering College, Chong Qing , (China)
Type
Published Article
Journal
International Journal of Advanced Network, Monitoring and Controls
Publisher
Exeley Inc.
Publication Date
Jan 01, 2018
Volume
3
Issue
1
Pages
62–68
Identifiers
DOI: 10.21307/ijanmc-2018-004
Source
Exeley
Keywords
License
Green

Abstract

In order to expand the application range of the intelligent traffic management system, and to solve the problem that the license plate positioning accuracy is low in the changing of the scene. On the basis of the analysis of previous methods advantages and disadvantages, applying deep learning model orientation method is proposed. The image expressed as graph of graph theory. Based on the principle of minimum spanning tree preliminary separate target objects in image of vehicle. Combined with the color, dimension, texture and match the similarity to choose, search and merger area in the image, suspicious area of the license plate is obtained. Using visual word package to express rectangular profile after coarse positioning. Using support vector machine (SVM) to classify and identify rectangular area of license plate. Accurate positioning license plate location is positioned accurately. The method of accuracy is 96.4% for 135 pieces of test sample positioning, strong anti-jamming.

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