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# Automatic vehicle license plate detection using VEDA

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### Automatic vehicle license plate detection using VEDA

1. 1. AUTOMATIC VEHICLE IDENTIFICATION USING VEDA GUIDED BY; MRS. MERCY MATHEW ASST. PROFESSOR CAARMEL ENGG. COLLEGE PRESENTED BY; ROJITH THOMAS MTECH-CE ROLL NO: 18
2. 2. INTRODUCTION  As number of automobiles grows rapidly, the traffic problems increase as well, for example, car theft, over speeding and running on the red light.  To avoid these problems, an efficient real time working vehicle identification system is needed.  Most widely accepted technique is License Plate Detection(LPD).  Based on Image processing by capturing license plate using cameras.  Applications: 1) crime prevention 2) parking and toll fee system 3) traffic data collections
3. 3. BASIC DIAGRAM  Three parts: 1) License Plate Detection 2) Character Segmentation 3) Recognition
4. 4. EXISTING algorithm  Difficult to process under complex conditions.  Kim et al Algorithm: statistical features and templates  Zimmermann and Mattas Algorithm: fuzzy logic  Sobel Algorithm: vertical edge extraction  Canny Algorithm: Vertical edge extraction  Abolghashemi Algorithm: low quality input  Zhang et al Algorithm: reduce complexity  Bai et al Algorithm: stationary and fixed background
5. 5. PROPOSED algorithm  Detection is by extracting vertical edges.  Low quality images are produced by using web camera.  Resolution is of 352 X 258 with 30 fps.  Steps: 1) Pre-processing. 2) Vertical Edge Detection. 3) Plate Extraction.
6. 6. 1. Pre processing  Process of generating binarized image from color image.  Two steps; 1) Color to gray image inversion(C2G). 2) Adaptive Thresholding. COLOR TO GRAY IMAGE CONVERSION  Converting color image into grayscale image. CAPTUARED IMAGE GRAY IMAGE
7. 7. ADAPTIVE THRESHOLDING  Gray image is converted into binarized image.  To get good adaptive threshold image , Integral image technique is used.  Earlier technique: Wellner’s Algorithm. a)Pixel is compared with avg. of neighboring pixels(S). b)Value of S=1/8 of (image). c)If current pixel is T% lower than S, then set to Black. d)Otherwise set to White. e)Value of T=0.15 of (image).  Limitation: Not suitable when samples are not evenly distributed in all directions(Moving System).
8. 8. INTEGRAL IMAGE FORMULATION  Window concept.  Image is as matrix with m rows and n columns.  Algorithm: Initially, summation of pixel values for every column is calculated as; sum(i)|j 1,0 ....... 1,n 2,0 g(x,y) = input values. sum(i) = all gray value for every column j through all rows i(i=0,1….m). . . . m,0 m,n
9. 9.  Integral image can be calculate as; where, IntrgImg(i,j) = integral image for pixel(i,j).  Next step is thresholding for each pixel. 1)Calculate intensity summation for each window. 2 subtraction and one addition is performed. i-s/2,j+s/2 i+s/2,j+s/2 i+s/2,j+s/2 i+s/2,j-s/2
10. 10.  Compare value g(i,j) with threshold value t(i,j).  After comparing we get output as; THRESHOLD IMAGE
11. 11. 2.VERTICAL EDGE EXTRACTION  Extracting the data by distinguishing the plate region.  Two steps: a) Unwanted Line Elimination Algorithm b) Vertical Edge Detection Algorithm UNWANTED LINE ELIMINATION ALGORITHM  To avoid long foreground lines and short noise edges besides LP region(Unwanted Lines)  Cases : 1) Horizontal with angle 0⁰(-). 2) Vertical with an angle 90⁰(|). 3) Line inclined at an angle 45⁰(/). 4) Line inclined at an angle 135⁰().
12. 12. CONCEPT:  Black pixel values are the background and White pixel values are the foreground.  A 3X3 mask is used throughout all image pixels from left to right and from top to bottom  Only black pixel values in the image are tested. b(x,y)
13. 13.  Different cases of converting the centre pixel into foreground  Output as THRESHOLD IMAGE ULEA OUTPUT
14. 14. VERTICAL EDGE BASED DETECTION ALGORITHM  To find beginning and end of each character  Concentrates on intersection of Black-White and WhiteBlack regions.
15. 15.  A 2X4 mask is used to process the image
16. 16.  Output is as;  Comparing with old edge extraction method SOBEL METHOD VEDA
17. 17. 3.PLATE EXTRACTION  To extract plate region and characters  Four steps: 1) Highlight Desired Details(HDD). 2) Candidate Region Extraction(CRE). 3) Plate Region Selection(PRS). 4) Plate Detection(PD).
18. 18. HIGHLIGHT DESIRED DETAILS  Performs NAND-AND operation for each two corresponding pixels values taken from ULEA &VEDA.  Connecting to vertical edges with black background. hd VEDA HDD
19. 19. NAND AND PROCEDURE  hd is the length between two edges.  Computed using test images.  Help to remove long foreground lines and noisy edges.  Process take place from top to bottom and left to right.  After this , plate region exists are highlighted.
20. 20. VEDA OUTPUT HDD OUTPUT
21. 21. CANDIDATE REGION EXTRACTION  To find exact LP region from the image.  Process divide into four steps. COUNT THE DRAWN LINES PER EACH ROW  No of horizontal lines in each rows are counted  Stored in a matrix variable : lines[a] ;a=0,1……m-1  Time consuming process. DIVIDE THE IMAGE INTO MULTIGROUPS  To avoid delay, images convert to multiple groups  Stored value in a variable : groups groups=height/C. C=CRE Constant (10)
22. 22. COUNT SATISFIED GROUP INDEXES AND BOUNDARIES  To eliminate unsatisfied groups which exists in the LP  A threshold value will be considered. Threshold>=1/15 of image height
23. 23. SELECTING BOUNDARIES OF CANDIDATE REGION  More than one region will be present  Drawing horizontal line above and below each candidate region OUTPUT AFTER CRE
24. 24. PLATE REGION SELECTION AND DETECTION  To extract one correct LP  Two steps 1. Selection of LP region 2. Making a vote. SELECTION OF LP REGION  Check blackness ratio of each pixels lies in candidate region  Each pixel is represent as Cregion
25. 25.  PRS factor is fixed and it was normally 0.5,0.4&0.3  After detecting region, the region will replaced by vertical lines. LP REGION
26. 26. CODE FLOW CHART
27. 27. MAKING A VOTE  Column with top and bottom neighbor have high blackness ratio will give a vote.  After voting section, the candidate region which have highest vote will be selected.  Finally plate will be detect and extracted.
28. 28. EXPERIMENTAL SETUP  Web camera should be in live condition.  2-4 meter distance.  IMAGES CLASSIFICATIONS EXPERIMENTAL CONDITIONS
29. 29. RESULT AND COMPARISON  Accuracy is higher than other LPD and algorithm useful for real time application
30. 30. Computation time of each stages Comparing with existing system
31. 31. CONCLUSION  Using web camera is for monitoring vehicles and also low resolution images are used  New and fast algorithm which is useful for real time requirements  Computation time is of 47.7 ms with an efficiency of 91.4%  Five to nine times faster than existing system
32. 32. REFERENCES  License plate recognition (LPR) technology : impact evaluation and community assessment for law enforcement  A Real-Time Mobile Vehicle License Plate Detection and Recognition; Kuo-Ming Hung and Ching-Tang Hsieh  Comparison of feature extractors in LPR; S N Hinda,K Marsuki,Y Rubiyah,O Kharuddin  www.wikipedia.com