Automatic vehicle license plate detection using VEDA
MRS. MERCY MATHEW
CAARMEL ENGG. COLLEGE
ROLL NO: 18
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
Based on Image processing by capturing license plate using
1) crime prevention
2) parking and toll fee system
3) traffic data collections
1) License Plate Detection
2) Character Segmentation
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
Detection is by extracting vertical edges.
Low quality images are produced by using web camera.
Resolution is of 352 X 258 with 30 fps.
2) Vertical Edge Detection.
3) Plate Extraction.
1. Pre processing
Process of generating binarized image from color image.
1) Color to gray image inversion(C2G).
2) Adaptive Thresholding.
COLOR TO GRAY IMAGE CONVERSION
Converting color image into grayscale image.
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).
INTEGRAL IMAGE FORMULATION
Image is as matrix with m rows and n columns.
Initially, summation of pixel values for every column is
g(x,y) = input values.
sum(i) = all gray value for every column j
through all rows i(i=0,1….m).
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.
Compare value g(i,j) with threshold value t(i,j).
After comparing we get output as;
2.VERTICAL EDGE EXTRACTION
Extracting the data by distinguishing the plate region.
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)
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().
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.
Different cases of converting the centre pixel into
VERTICAL EDGE BASED DETECTION ALGORITHM
To find beginning and end of each character
Concentrates on intersection of Black-White and WhiteBlack regions.
Output is as;
Comparing with old edge extraction method
To extract plate region and characters
1) Highlight Desired Details(HDD).
2) Candidate Region Extraction(CRE).
3) Plate Region Selection(PRS).
4) Plate Detection(PD).
HIGHLIGHT DESIRED DETAILS
Performs NAND-AND operation for each two
corresponding pixels values taken from ULEA &VEDA.
Connecting to vertical edges with black background.
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
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
C=CRE Constant (10)
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
SELECTING BOUNDARIES OF CANDIDATE REGION
More than one region will be present
Drawing horizontal line above and below each candidate
OUTPUT AFTER CRE
PLATE REGION SELECTION AND DETECTION
To extract one correct LP
1. Selection of LP region
2. Making a vote.
SELECTION OF LP REGION
Check blackness ratio of each pixels lies in candidate
Each pixel is represent as Cregion
PRS factor is fixed and it was normally 0.5,0.4&0.3
After detecting region, the region will replaced by vertical
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.
Web camera should be in live condition.
2-4 meter distance.
RESULT AND COMPARISON
Accuracy is higher than other LPD and algorithm useful
for real time application
Computation time of each stages
Comparing with existing system
Using web camera is for monitoring vehicles and also
low resolution images are used
New and fast algorithm which is useful for real time
Computation time is of 47.7 ms with an efficiency of
Five to nine times faster than existing system
License plate recognition (LPR) technology : impact
evaluation and community assessment for law
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