NTIT IMD 1 Speaker: Ching-Hao Lai( 賴璟皓 ) Author: Hongliang Bai, Junmin Zhu and Changping Liu Source: Proceedings of IEEE on Intelligent Transportation Systems, Volume 2, Oct , 2003, P.P P.P A Fast License Plate Extraction Method on Complex Background Date: 2004/10/6

NTIT IMD 2 Author: Yanamura, Y.; Goto, M.; Nishiyama, D.; Soga, M.; Nakatani, H.; Saji, H.; Nakatani, H.; Saji, H.; Source: Intelligent Vehicles Symposium, Proceedings. IEEE, June 9-11, 2003 Proceedings. IEEE, June 9-11, 2003 Pages: Pages: Extraction and Tracking of the License Plate Using Hough Transform and Voted Block Matching

NTIT IMD 3 Outline Introduction Introduction Overview of the proposed system Overview of the proposed system Experimental Results Experimental Results Conclusion Conclusion

NTIT IMD 4 LPR has turned out to be an important research issue. LPR has turned out to be an important research issue. LPR system consists of three parts: LPR system consists of three parts: License plate detection License plate detection Character segmentation Character segmentation Character recognition Character recognition A fast license plate localization algorithm for monitoring the highway ticketing system. A fast license plate localization algorithm for monitoring the highway ticketing system. Introduction(1/2)

NTIT IMD 5 LP detect method overview: LP detect method overview: Morphological operations Morphological operations Edge extraction Edge extraction Combination of gradient features Combination of gradient features Neural Network for color classification Neural Network for color classification Vector quantization Vector quantization Back-propagation neural network (BPNN) Back-propagation neural network (BPNN) Introduction(2/2)

NTIT IMD 6 Input Image Input Image Vertical Edge Detection Vertical Edge Detection Edge Density Map Generation Edge Density Map Generation Binarization and Dilation Binarization and Dilation License Plate Location License Plate Location Output Region Output Region Overview

NTIT IMD 7 Horizontal Sobel Filter Horizontal 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 Filter VerticalSobel 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)

NTIT IMD 8 Sobel Filter Horizontal Sobel 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*1) Sobel Filter Vertical g(v)=|(30*1+33*2+36*1) -(119*1+115*2+114*1)|=331 -(119*1+115*2+114*1)|=331 Vertical Edge Detection(2/3)

NTIT IMD 9 Vertical edge detector is better than horizontal edge detector. Vertical edge detector is better than horizontal edge detector. Vertical Edge Detection(3/3)

NTIT IMD 10 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 mapd(I,j) represents the edge density map Edge Density Map Generation(1/2)

NTIT IMD 11 Edge Density Map Generation(2/2)

NTIT IMD 12 Binarization(1/3) Otsu Histogram Threshold: Otsu Histogram Threshold: Histogram-derived thresholds Histogram-derived thresholds

NTIT IMD 13 Binarization(2/3) : 變異數 : 變異數 : 概率 ( 加權 ) 求 最小值 : 概率 ( 加權 ) 求 最小值

NTIT IMD 14 Binarization(3/3)

NTIT IMD 15 Dilation(1/4) Before dilation, we use a nonlinear filter Before dilation, we use a nonlinear filter remove narrow horizontal lines. remove narrow horizontal lines. If Bottom-Top<T (Threshold=5) then If Bottom-Top<T (Threshold=5) then For(i=Top;i<=Bottom;i++) p(i)=0 For(i=Top;i<=Bottom;i++) p(i)=0

NTIT IMD 16 Dilation(2/4)

NTIT IMD 17 Dilation(3/4) We dilate the image use a horizontal mask. We dilate the image use a horizontal mask. If Right-Left<T (Threshold=9) then For(i=Left;I<=Right;i++) p(i)=255 For(i=Left;I<=Right;i++) p(i)=255

NTIT IMD 18 Dilation(4/4)

NTIT IMD 19 License Plate Location(1/2) Connected Component Analysis Connected Component Analysis Feature Extraction Feature Extraction Aspect ratio (R) = W / H Aspect 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 connected density Combination of candidate regions by the connected density Getting Final Candidate regions Getting Final Candidate regions

NTIT IMD 20 License Plate Location(2/2) Blue Block Width=4 Height=6

NTIT IMD 21 Data Source: Data Source: 478 real scene images acquired from the real highway ticketing station 478 real scene images acquired from the real highway ticketing station Resolution: 768x534 Resolution: 768x534 Different Light condition: Different Light condition: cloudy, sunny, daytime, night time cloudy, sunny, daytime, night time Different kind of vehicle: Different kind of vehicle: van, truck, car van, truck, car 459 of 478 (96%) image were successful detect 100ms per image 459 of 478 (96%) image were successful detect 100ms per image Experimental Results

NTIT IMD 22 Conclusion A fast license plate localization scheme is presented in the paper. A fast license plate localization scheme is presented in the paper. The most serious shortcoming of our method is in falling to locate the license plate that is badly deficient. The most serious shortcoming of our method is in falling to locate the license plate that is badly deficient. It is relatively robust to variations of the lighting conditions and different kinds of vehicle. It is relatively robust to variations of the lighting conditions and different kinds of vehicle.