Automatic vehicle license plate detection using VEDA
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.
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.
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
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
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