<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Multiple Vehicle License Plate Location in Complex Background

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Multiple Vehicle License Plate Location in Complex Background"}

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...

Multiple Vehicle License Plate Location in Complex Background

Publicado en línea: 07 may 2018

Páginas: 62 - 68

DOI: https://doi.org/10.21307/ijanmc-2018-004

Palabras clave
License Plate Position, Complex Background, Bag-of-view, Support Vector Machine, License Plate Vertical Texture

© Yaxin Zhao 2018 et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Multiple Vehicle License Plate Location in Complex Background

Publicado en línea: 07 may 2018

Páginas: 62 - 68

DOI: https://doi.org/10.21307/ijanmc-2018-004

Palabras claveLicense Plate Position, Complex Background, Bag-of-view, Support Vector Machine, License Plate Vertical Texture

© Yaxin Zhao 2018 et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Palabras clave
License Plate Position, Complex Background, Bag-of-view, Support Vector Machine, License Plate Vertical Texture