vehicle number plate detection for traffic control · 2019. 5. 7. · a. vehicle image captured by...

VEHICLE NUMBER PLATE DETECTION FOR

TRAFFIC CONTROL 1V.V.K.Raju,

2M.N.Manikanta,

3L.Rajani,

4M.Sajida,

5N.Sandeep,

6P.Revanth

Department of Electronics and Communication Engineering,

Anil Neerukonda Institute of Technology and Sciences

Andhra pradesh, India – 531162 1

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

Abstract — Basically video surveillance system is used for security purpose as well as monitoring systems. But detection of moving object is a challenging part of video surveillance. Video surveillance system is used for Home security, Military applications, Banking /ATM

security, Traffic monitoring etc. Now a days due to decreasing costs of high quality video surveillance systems, human activity detection

and tracking has become increasingly in practical. Accordingly, automated systems have been designed for numerous detection tasks, but

the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. The detection of Indian

vehicles by their number plates is the most interesting and challenging research topic from past few years. It is observed that the number

plates of vehicles are in different shape and size and also have different colour in various countries. This project proposes a method for the detection and identification of vehicle number plate that will help in the detection of number plates

of authorized and unauthorized vehicles. This project presents an approach based on simple but efficient morphological operation and

Sobel edge detection method. This approach is simplified to segment all the letters and numbers used in the number plate by using

bounding box method. After segmentation of numbers and characters present on number plate, template matching approach is used to the

recognition of numbers and characters. The concentrate is given to locate the number plate region properly to segment all the number and

letters to identify each number separately.

Keywords—Number plate recognition, segmentation, Template matching, sobel edge detection.,video surveillance

I. INTRODUCTION

License Plate Recognition (LPR) is a combination of image processing, character segmentation and recognition technologies

used to identify vehicles by their license plates. Since only the license plate information is used for identification, this

technology requires no additional hardware to be installed on vehicles. LPR technology is constantly gaining popularity,

especially in security and traffic control systems. License Plate Recognition Systems are utilized frequently for access control

in buildings and parking areas, law enforcement, stolen car detection, traffic control, automatic toll collection and marketing

research.

LPR applications apply image processing and segmentation algorithms for license plate extraction, and each operation

involves lots of computation. Government regulations standards employed in the license plates can reduce the computational

requirements substantially and improve the accuracy. Constraints contain range of values instead of exact measures, since the

license plate text size, style and orientation can vary substantially in different images.

The license plate recognition systems have two main points: the quality of license plate recognition software with recognition

algorithms used and the quality of imaging technology, including camera and lighting.

JASC: Journal of Applied Science and Computations

Volume VI, Issue V, May/2019

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Elements to be considered: maximum recognition accuracy, achieve faster processing speed, handling as many types of

plates, manage the broadest range of image qualities and achieve maximum distortion tolerance of input data.

Ideally, for extreme conditions and with serious problems of normal visibility, would have special cameras ready for such an

activity, such as infrared cameras that are much better to address these goals and achieve better results. This is because the

infrared illumination causes reflection of light on the license plate made of special material which causes a different light in

that area of the image relative to the rest of it, causing it to be easier to detect.

II. STRUCTURE OF THE PROPOSED SYSTEM

The system presented is designed to recognize license plates from vehicle. Input to the system is an image acquired by a

camera that consists of a license plate and its output is the recognition of characters on the license plate in a separate notepad

window.

The system consists of the standard six main modules in an LPR system, viz. Estimation of vehicle speed, Image acquisition,

License plate extraction, License plate segmentation and License plate recognition. The first task acquires the image. The

second task extracts the region that contains the license plate. The third task isolates the characters, letters and numerals (total

of 10 digits), as in the case of Indian License Plates. The last task identifies or recognizes the segmented characters.

III. METHODOLOGY

Block Diagram of Number Plate Recognition (NPR) is shown in Fig. 1:

A. Vehicle Image Captured By Camera :

The image of the vehicle whose number plate is to be identified is captured using digital camera. Once the moving vehicle is

detected, it’s velocity is measured using background subtraction method.



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A. BACKGROUND SUBTRACTION METHOD:

Background subtraction is a useful and effective method for detecting moving objects in video images. Since this method

assumes that image variations are caused only by moving objects (i.e., the background scene is assumed to be stationary),

however, its applicability is limited. The rationale in the approach is that of detecting the moving objects from the difference

between the current frame and a reference frame, often called "background image", or "background model". Background

subtraction is mostly done if the image in question is a part of a video stream. There are many challenges in developing a

good background subtraction algorithm. First, it must berobust against changes in illumination. Second, it should avoid

detecting non-stationary background objects such as moving leaves, rain, snow, and shadow cast by moving objects. Finally,

its internal background model should react quickly to changes in background such as starting and stopping of vehicles.

B. Extraction of Number Plate Location:

In this step the number plate is extracted by firstly converting RGB Image i.e., the captured image to Gray Scale Image. Here

mathematical morphology is used to detect the region and Sobel operator are used to calculate the threshold value. After this

we get a dilated image. Then imfill function is used to fill the holes so that we get a clear binary image.

C. Segmentation of Plate Character:

Here bounding box technique is used for segmentation. The bounding box is used to measure the properties of the image

region. The basic step in recognition of vehicle number plate is to detect the plate size. Here the segmented image is

multiplied with gray scale image so that we only get the number plate of the vehicle.

SOBEL OPERATOR:

The operator consists of a pair of 3×3 convolution kernels as shown in Figure 1. One kernel is simply the other rotated by

90°.



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These kernels are designed to respond maximally to edges running vertically and horizontally relative to the pixel grid, one

kernel for each of the two perpendicular orientations. The kernels can be applied separately to the input image, to produce

separate measurements of the gradient component in each orientation (call these Gx and Gy). These can then be combined

together to find the absolute magnitude of the gradient at each point and the orientation of that gradient. The gradient

magnitude is given by:

Typically, an approximate magnitude is computed using:

which is much faster to compute.

The angle of orientation of the edge (relative to the pixel grid) giving rise to the spatial gradient is given by:

D. Character Segmentation:

Segmentation is one of the most important processes in the number plate recognition, because all further steps rely on it. If

the segmentation fails, a character can be improperly divided into two pieces, or two characters. The ultimate solution on this

problem is to use bounding box technique. The bounding box is used to measure the properties of the image region. Once a

bounding box created over each character and numbers presented on number plate, each character & number is separate out

for recognition of number plate The result of operation is shown in Fig.11



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A. Character recognition and display of result:

It is employed for the purpose of conversion of images of text into characters. Number plate recognition is now used to

compare the each individual character against the complete alphanumeric database using template matching. The

matching process moves the template image to all possible positions in a larger source image and computes a numerical

index that indicates how well the template matches the image in that position. Matching is done on a pixel by pixel basis.

The template is of size 42 × 24 as shown in Fig.13.Since the template size is fixed, it leads to accurate recognition.

IV. MATLAB RESULTS

The final result of ANPR system is shown in Fig.

It displays number plate of the desired vehicle.

V. ADVANTAGES

o Reduces Traffic near the toll checkpoint areas.

o Maintains the record of the total toll collected.

o Reduces the overhead of collecting physical cash from commuters.

o Reduces fraudulent behaviour at toll checkpoints. E. Implementation cost is less.

o Acknowledgement of the payment in the form of SMS.

VI. APPLICATIONS OF NPR SYSTEM

1. Parking :- The NPR is used to automatically enter prepaid members and calculate parking fee for nonmembers.

2. Access control :- A gate automatically opens for authorized members in a secured area, thus replacing or assisting the

security guard.

3. Tolling :- The car number is used to calculate the travel fee in a toll-road or used to double check the ticket.

4. Border Security :- The car number is registered in the entry or exits to the country and used to monitor the border

crossings.



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5. Traffic Control :- The vehicles can be directed to different lanes according to their entry permits. The system reduces the

traffic congestions and number of attendants.

6. Airport Parking :- In order to reduce ticket frauds or mistakes, the NPR unit is used to capture the number plate and image

of the car.

VII. CONCLUSION

In this paper, we presented application software designed for the recognition of car license plate. Firstly we extracted the

plate location, then we separated the plate characters individually by segmentation and finally applied template matching with

the use of correlation for recognition of plate characters. Some of the applications of our system are tracking stolen cars,

traffic monitoring, managing parking toll, and red light violation enforcement. The main objective of this paper is to produce

better results in vehicle tracking by overcoming those above mentioned difficulties. Some of the issues like stains, blurred

regions, differences in font styles and sizes are need to be taken care of. This work can be further extended to minimize the

errors due to them.

REFERENCES

[1] R.Radha1 and C.P.Sumathi2, “A Novel approach to extract text from license plate of vehicle”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.4, August 2012

[2] Shen Zheng Wang & His-Jian Lee “Detection and Recognition of License Plate Characters with Different Appearances”,IEEE Intelligent Transportation Systems, Proceedings 2003 , vol.2 , Page(s): 979 – 984.

[3] Humayun Karim Sulehria, Ye Zhang, Danish Irfan, Atif Karim Sulehria, “ Vehicle Number Plate Recognition Using Mathematical Morphology and Neural Networks”, WSEAS TRANSACTIONS on COMPUTERS, Volume 7,ISSN: 1109-2750, Issue 6, June 2008.

[4] Dr. P.K.Suri, Dr. Ekta Walia, Er. Amit Verma,” Vehicle Number Plate Detection using Sobel Edge Detection Technique”, International Journal of Computer Science and Technology, ISSN : 2229 – 4333, IJCST Vol. 1, Issue 2, December 2010.

[5] Kumar Parasuraman and P.Vasantha Kumar, “ An Efficient Method for Indian Vehicle License Plate Extraction and Character Segmentation”, IEEE International Conference on Computational Intelligence and Computing Research,2010. [6] Lekhana G.C, R.Srikantaswamy ,“Real time license plate recognition system”, International Journal of Advanced Technology & Engineering Research (IJATER), National Conference on Emerging Trends in Technology (NCETTech) ISSN, Volume 2, Issue 4, ISSN No: 2250-3536, July 2012.



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vehicle number plate detection for traffic control · 2019. 5. 7. · a. vehicle image captured by...

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