speaker: ching-hao lai( 賴璟皓 )

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Speaker:Speaker: Ching-Hao Lai( Ching-Hao Lai( 賴璟皓賴璟皓 ))

Author:Author: Hongliang Bai, Junmin Zhu and Changping Liu Hongliang Bai, Junmin Zhu and Changping Liu

Source:Source: Proceedings of IEEE on Intelligent Transportation Proceedings of IEEE on Intelligent Transportation Systems, Volume 2, Oct. 12-15, 2003, Systems, Volume 2, Oct. 12-15, 2003,

P.P. 985 - 987 P.P. 985 - 987

A Fast License Plate Extraction A Fast License Plate Extraction Method on Complex BackgroundMethod on Complex Background

Date: 2004/10/6

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Author:Author: Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.; Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.; Nakatani, H.; Saji, H.;Nakatani, H.; Saji, H.;Source:Source: Intelligent Vehicles Symposium, 2003. Intelligent Vehicles Symposium, 2003. Proceedings. IEEE , June 9-11, 2003Proceedings. IEEE , June 9-11, 2003 Pages:243 - 246Pages:243 - 246

Extraction and Tracking of the License Plate Extraction and Tracking of the License Plate Using Hough Transform and Voted Block Using Hough Transform and Voted Block

MatchingMatching

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OutlineOutline IntroductionIntroduction Overview of the proposed systemOverview of the proposed system Experimental ResultsExperimental Results ConclusionConclusion

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LPR has turned out to be an important LPR has turned out to be an important research issue.research issue.

LPR system consists of three parts:LPR system consists of three parts:

License plate detectionLicense plate detection

Character segmentationCharacter segmentation

Character recognitionCharacter recognition A fast license plate localization algorithm A fast license plate localization algorithm

for monitoring the highway ticketing for monitoring the highway ticketing system.system.

Introduction(1/2)Introduction(1/2)

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LP detect method overview:LP detect method overview:

Morphological operationsMorphological operations

Edge extractionEdge extraction

Combination of gradient featuresCombination of gradient features

Neural Network for color classificationNeural Network for color classification

Vector quantizationVector quantization

Back-propagation neural network (BPNN)Back-propagation neural network (BPNN)

Introduction(2/2)Introduction(2/2)

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Input ImageInput Image

Vertical Edge DetectionVertical Edge Detection Edge Density Map GenerationEdge Density Map Generation Binarization and DilationBinarization and Dilation License Plate LocationLicense Plate Location

Output RegionOutput Region

OverviewOverview

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Horizontal Sobel FilterHorizontal Sobel Filter

g(h)=|[f(i-1,j-1)+2f(i-1,j)+f(i-1,j+1)]g(h)=|[f(i-1,j-1)+2f(i-1,j)+f(i-1,j+1)]

-[f(i+1,j-1)+2f(i+1,j)+f(i+1,j+1)]|-[f(i+1,j-1)+2f(i+1,j)+f(i+1,j+1)]|

VerticalSobel FilterVerticalSobel Filter

g(v)=|[f(i-1,j-1)+2f(i,j-1)+f(i+1,j-1)]g(v)=|[f(i-1,j-1)+2f(i,j-1)+f(i+1,j-1)]

-[f(i-1,j+1)+2f(i,j+1)+f(i+1,j+1)]|-[f(i-1,j+1)+2f(i,j+1)+f(i+1,j+1)]|

Vertical Edge Detection(1/3)Vertical Edge Detection(1/3)

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Sobel Filter HorizontalSobel Filter Horizontal

g(h)=|(30*1+33*2+119*1)g(h)=|(30*1+33*2+119*1)

-(36*1+115*2+114*1)|=165-(36*1+115*2+114*1)|=165 Sobel Filter Vertical g(v)=|(30*1+33*2+36*Sobel Filter Vertical g(v)=|(30*1+33*2+36*

1)1)

-(119*1+115*2+114*1)|=331-(119*1+115*2+114*1)|=331


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Vertical edge detector is better than Vertical edge detector is better than horizontal edge detector.horizontal edge detector.


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Density Formulation: Density Formulation: • 3 X 15 block and center at (I,j)3 X 15 block and center at (I,j)

• d(I,j) represents the edge density d(I,j) represents the edge density mapmap

Edge Density Map Edge Density Map Generation(1/2)Generation(1/2)


Edge Density Map Generation(2/2)Edge Density Map Generation(2/2)


Binarization(1/3)Binarization(1/3)

Otsu Histogram Threshold:Otsu Histogram Threshold:

Histogram-derived thresholdsHistogram-derived thresholds



:: 變異數變異數

:: 概率概率 (( 加權加權 )) 求最小值求最小值


Dilation(1/4)Dilation(1/4) Before dilation, we use aBefore dilation, we use a nonlinear filter nonlinear filter

remove narrow horizontal lines.remove narrow horizontal lines.

If Bottom-Top<T (Threshold=5) thenIf Bottom-Top<T (Threshold=5) then

For(i=Top;i<=Bottom;i++) p(i)=0For(i=Top;i<=Bottom;i++) p(i)=0


Dilation(2/4)Dilation(2/4)


Dilation(3/4)Dilation(3/4) We dilate the image use a We dilate the image use a horizontal horizontal

mask.mask.

If Right-Left<T (Threshold=9) thenIf Right-Left<T (Threshold=9) then

For(i=Left;I<=Right;i++) p(i)=255For(i=Left;I<=Right;i++) p(i)=255


Dilation(4/4)Dilation(4/4)


License Plate Location(1/2)License Plate Location(1/2) Connected Component AnalysisConnected Component Analysis Feature ExtractionFeature Extraction

Aspect ratio (R) = W / HAspect ratio (R) = W / H

Area (A) = W x H Area (A) = W x H

Density (D) = N / ( W x H )Density (D) = N / ( W x H ) Combination of candidate regions by the Combination of candidate regions by the

connected densityconnected density

Getting Final Candidate regionsGetting Final Candidate regions


License Plate Location(2/2)License Plate Location(2/2)Blue Block Width=4 Height=6Blue Block Width=4 Height=6


Data Source:Data Source:

478 real scene images acquired from the real 478 real scene images acquired from the real highway ticketing stationhighway ticketing station

Resolution: 768x534Resolution: 768x534 Different Light condition:Different Light condition:

cloudy, sunny, daytime, night timecloudy, sunny, daytime, night time Different kind of vehicle:Different kind of vehicle:

van, truck, carvan, truck, car 459 of 478 (96%) image were successful detect 459 of 478 (96%) image were successful detect

100ms per image100ms per image

Experimental ResultsExperimental Results


ConclusionConclusion A fast license plate localization scheme is A fast license plate localization scheme is

presented in the paper.presented in the paper. The most serious shortcoming of our method is The most serious shortcoming of our method is

in falling to locate the license plate that is in falling to locate the license plate that is badly deficient.badly deficient.

It is relatively robust to variations of the It is relatively robust to variations of the lighting conditions and different kinds of lighting conditions and different kinds of vehicle.vehicle.

speaker: ching-hao lai( 賴璟皓 )

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