License plate detection

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. 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. BASIC DIAGRAM
 Three parts:
1) License Plate Detection
2) Character Segmentation
3) Recognition

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. 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. 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. 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. 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.  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.  Compare value g(i,j) with threshold value t(i,j).
 After comparing we get output as;
THRESHOLD IMAGE

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. 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.  Different cases of converting the centre pixel into
foreground
 Output as
THRESHOLD IMAGE
ULEA OUTPUT

14. VERTICAL EDGE BASED DETECTION ALGORITHM
 To find beginning and end of each character
 Concentrates on intersection of Black-White and WhiteBlack regions.

15.  A 2X4 mask is used to process the image

16.  Output is as;
 Comparing with old edge extraction method
SOBEL METHOD
VEDA

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. 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. 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. VEDA OUTPUT
HDD OUTPUT

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. 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. 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. 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.  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. CODE
FLOW CHART

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. EXPERIMENTAL SETUP
 Web camera should be in live condition.
 2-4 meter distance.
 IMAGES
CLASSIFICATIONS
EXPERIMENTAL CONDITIONS

29. RESULT AND COMPARISON
 Accuracy is higher than other LPD and algorithm useful
for real time application

30. Computation time of each stages
Comparing with existing system

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. 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