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Vehicle License Plate Detection Algorithm Based on Statistical Characteristics in HSI Color Model. Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) [email protected] Introduction.

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Vehicle License Plate Detection Algorithm Based on Statistical Characteristics in HSI Color Model

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Vehicle license plate detection algorithm based on statistical characteristics in hsi color model l.jpg

Vehicle License Plate Detection Algorithm Based on Statistical Characteristics in HSI Color Model

Instructor : Dr. K. R. Rao

Presented by: Prasanna Venkatesh Palani

(1000660520)

[email protected]


Introduction l.jpg

Introduction

  • With constantly increasing traffic on roads, there is a need for intelligent traffic management system.

  • License plate (LP) detection is widely used for detecting speeding cars, security control, traffic law enforcement and electronic toll collection.

  • License plate detection can be performed via various approaches such as

    • Vector quantization

    • Gabor transform

    • Optical character recognition

    • Neural networks.


Intoduction l.jpg

Intoduction

  • License plate detection is a two step process

    • Detecting the plate.

    • Character recognition to identify the characters on the plate.

    • This project discusses a method to select automatically statistical threshold value in HSI color space for detecting candidate regions.

    • This will lead to a framework which unifies detection, tracking and recognition of license plate.


Rgb and hsi model l.jpg

RGB and HSI Model

RGB Model

  • In The RGB color model, different colors can be reproduced by additively combining red, green, and blue in different ways. In a general sense, the RGB color model describes our perception of color. Three types of receptors in the retina of the human eye have peak sensitivities corresponding to these three primary colors. (Fig 1).

  • The RGB color model represents colors within a cubic volume defined by orthogonal Red, Green, and Blue axes .

  • Black is at the origin of the coordinate system (R=G=B=0)

  • White is at the opposite corner of the cube (R=G=B=255).

  • The diagonal connecting the black and white corners (gray dashed line) contains the range of neutral gray levels.


Slide5 l.jpg

HSI Model

  • The HSI color model, represents colors within a double-cone space. (Fig 2).

  • The vertical axis is intensity, which represents variations in the lightness and darkness of a color.

  • The 0 intensity level is black; full intensity is white.

  • HSI values elsewhere along the intensity axis represent different levels of gray.

  • On any horizontal slice through the model space, the hue (or “color” of the color) varies around the slice, and the saturation (the purity of the color) increases radially outward from the central intensity axis.

  • In the HSI color model, intensity makes no contribution to the color.


Algorithm for detecting license plate region fig 3 l.jpg

Algorithm for detecting license plate region (Fig.3)

  • The algorithm consists of three parts

    • Candidate regions are identified using HSI color model

    • The geometrical properties of the license plate such as area, bounding box, aspect ratio, are used to filter the candidate region.

    • Predetermined alphanumeric character is determined by decomposing the candidate region.


Color segmentation l.jpg

Color Segmentation

1) Input Image (RGB) is converted to HSI color model[1][6].

2) Plate information is used to identify the candidate region.

3) Shape properties of the candidate region are used to reduce the number of license plate like candidate regions.


Color segmentation8 l.jpg

Color Segmentation


Steps for license plate detection l.jpg

Steps for license plate detection

As shown in Fig 5, the license plate detection involves the following steps

Input image

Color segmentation result

Detected candidate after filtering

Candidate region detection


Algorithm for candidate decomposition l.jpg

Algorithm For Candidate Decomposition

  • Extracting character region.

  • Vertical position histogram with border.

  • Horizontal position histogram without border.

  • Normalization of candidate region.

  • Obtain vertical position histogram which contains peaks for predetermined alphanumeric character in license plate region.

  • Character extraction.


References l.jpg

References

  • Y. Wang et al, “Study on HSI Color Model-Based Fruit Quality Evaluation”, International Congress on Image and Signal Processing (CISP), DOI:10.1109/CISP.2010.5647943, Vol 6 , pp 2677 – 2680, 2010.

  • H. Lim et al ,“An Efficient Method of Vehicle License Plate Detection Based on HSI Color Model and Histogram”, Next-generation Applied Intelligence, DOI: 10.1007/978-3-642-02568-6,Vol 5579/2009, pp 66-75,2009.

  • K. Deb, S. Kang and K. Jo, “Statistical Characteristics in HSI Color Model and Position Histogram Based Vehicle License Plate Detection”, Intelligent Service Robotics, DOI: 10.1007/s11370-009-0043-x, Vol 2, pp 173-186, June 2009.

  • M.Donosser , C.Arth and H.Bischof , “Detecting, Tracking and Recognizing license plates. In: Y.Yagi, S.B.Kang, I.S.Kweon and H.Zha, (eds.) ACCV2--7, Part II. LNCS,vol 4844, pp 447-456. Springer, Heidelberrg, 2007.

  • Y. Li and M. Wang. “Novel and fast Algorithms of License Plate Locations and Extractions,” IEEE International Conference on Information and Automation (ICIA),DOI : 10.1109/ICINFA.2010.5512273,pp 2462 - 2465, 2010.


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References

6. R. C. Gonzalez, and R. E. Woods, Digital Image Processing, Third Edition. Prentice Hall, N.J., 2008.

7. O.Serkan and E.Ergun, "Automatic Vehicle Identification by Plate Recognition," Proceedings of World Academy of Science,Engineering and Technology, Vol 9, pp.222- 225, Nov 2005.

8. M.M.I.Chacon and S.A.Zimmerman , "License plate location based on a dynamic PCNN scheme," Proceedings of the International Joint Conference onNeural Networks, Vol 2, pp 1195- 1200 , 20-24 July 2003.

9. C.P.Marques et al, “License Vehicle Plates Localization Using Maximum Correlation”, Structural, Syntactic, and Statistical Pattern Recognition Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol 3138/2004, pp 470-476, 2004.


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