idea of the paper is to monitor, control the entry of unauthorized vehicles
automatically.  Now days, the vehicle
entry is increasing day by day in all organization premises. The security is
unable to prevent the entry of unauthorized vehicle during peak hours. For this
purpose automatic vehicle recognition system is required. This can be achieved
by Image Processing technique.



Nowadays in many organizational premises
it is required to monitor and control the entry of the authenticated vehicles.
Currently it has been done manually. Since this process is time consuming and
less accurate, we planned to automate the process. This paper involves in
monitoring and controlling the authenticated vehicle entry automatically by
using image processing technique. The data of the authenticated vehicles is
stored previously in the database. When the vehicle enters into the premises
gate the image of the number plate is captured and compared with the database
stored in the PC. After receiving the input, it compares with the image
processing tool.  The corresponding
actions will be performed by the Arduino kit.

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IR proximity sensor will sense the entry
of the vehicle and send the signal to the Arduino kit. Then, image is acquired
by the webcam and it is processed by MATLAB. 
The database is defined previously, which consists of data of the
authenticated vehicle.  When a vehicle
enters into an organization premises the number plate is captured using camera
placed at a specified angle. The captured image is processed, where it compares
the template of the input image and the database.  When it matches, no action is performed, and
if does not matches then the gate is closed, an alarm will be triggered, the
vehicle gets diverted to security office and also the unauthorized vehicle will
be displayed.


Since the setup is compact and it is
cost effective. It is also a time efficient method to monitor and control the
entry of the vehicle. Also, the accuracy of the process tends to be high. It
can also be used in schools, colleges, IT companies and many organization
premises.  Hence a highly secured Vehicle
Monitoring System is proposed.








A proximity sensor is a sensor able to detect the presence of nearby
objects without any physical contact. Passive
infrared sensors are basically Infrared detectors. Passive infrared sensors do
not use any infrared source and detects energy emitted by obstacles in the
field of view.  Quantum type infrared
detectors offer higher detection performance and are faster than thermal type
infrared detectors. The photosensitivity of quantum type detectors is
wavelength dependent. Intrinsic type quantum detectors are photoconductive
cells and photovoltaic cells.  Active infrared sensors consist of two elements: infrared
source and infrared detector. Infrared sources include an LED or infrared laser
diode. Infrared detectors include photodiodes or phototransistors. The energy
emitted by the infrared source is reflected by an object and falls on the
infrared detector.  The principle of an
IR sensor working as an Object Detection Sensor can be explained using the
following figure. An IR sensor consists of an IR LED and an IR Photodiode;
together they are called as Photo – Coupler or Opto – Coupler.




The ARDUINO is a microcontroller
board based on ATmega 328P. Its operating voltage is 5V. The recommended input
voltage is 7-12V and the limited input voltage is 6-20V.


It consists of 14 digital
input/output pins of which 6 provide PWM output. It also has 6 analog input
pins, a 16MHz quartz crystal, a USB connection, a power jack and a reset
button. The DC current provided per I/O pin is 20mA and DC current for 3.3V pin
is 50mA. ATmega328P has 32KB flash memory of which 0.5KB used by boot loader.
The Arduino Uno board can be powered via the USB connection or with an external
power supply. External power supply may either from an AC-to-DC adapter or a




The voltage
reference is provided by the IOREF pin on the Arduino board. This operates the



A DC motor is any of a class of
electrical machines that converts direct current electrical power into
mechanical power.  The most common types
rely on the forces produced by magnetic fields. 
Nearly all types of DC motors have some internal mechanism, either
electromechanical or electronic; to periodically change the direction of
current flow in part of the motor.  Most
types produce rotary motion; a linear motor directly produces force and motion
in a straight line.  DC motors are widely
used.  A DC motor’s speed can be
controlled over a wide range, using either a variable supply voltage or by
changing the strength of current in its field windings.  Small DC motors are used in tools, toys and
appliances.  The universal motor can
operate on direct current but is a lightweight motor used for portable power
tools and appliances.  Large DC motors
are used in propulsion of electric vehicles, elevator and hoists, or in drives
for steel rolling mills. 






          L293d is a Motor Driver integrated circuit which is used to
drive DC motors rotating in either direction. 
It is a 16-pin IC which can control a set of two DC motors
simultaneously. The l293d uses 5V for its own power and external power source
is needed to drive the motors, which can be up to 36V and draw up to 600Ma.


are two Enable pins on l293d.  They are
Pin 1 (left H-bridge) and pin 9 (right H-bridge).   To drive the corresponding motor, pin 1 or 9
need to be set to HIGH.  If either pin 1
or 9 goes low then the motor in the corresponding section will suspend
working.  The four input pins for the
l293d are pin 2 and 7 on the left and pin 15 and 10 on the right.  Left input pins will regulate the rotation of
motor connected on the left and right input for motor on the right hand side.  The motors are rotated on the basis of the
inputs provided at the input pins as LOGIC 1 or LOGIC 0.



Image processing is processing of images using
mathematical operations by using any form of signal
processing for which the
input is an image, a series of images, or a video, such as a photograph or video
frame; the output of image processing may be either an image or a set of
characteristics or parameters related to the image. Most
image-processing techniques involve treating the image as a two-dimensional signal and applying standard
signal-processing techniques to it. Image processing usually refers to digital image processing.



Vehicle Entry
Monitoring System have been developed as a major tool for analyzing and also
handling the entry of vehicles in Colleges, Schools and IT Sectors etc. These
systems attempt to facilitate the problem of identification of cars, via
various techniques which mainly rely on automated (rather than manual)
algorithms. Image processing is one of these techniques which deal with images/video
sequences taken from vehicles. One unique property that can be taken into
account for identifying all vehicles is their number plate.


In this
project, an approach of automatic number plate recognition system based on
connected component extraction algorithm concept is proposed. Input car number
plate images are collected from surrounding and taken into processing. Number
plate extraction is done by using connected component extraction algorithm and
the output image is given to recognition part. Trained with edge orientation
histograms features of standard character and number dataset. The text areas
are separated by using MSER algorithm and each character is recognized.


In future, the
unauthorized vehicle number plate image will be displayed and recognition
accuracy can be improved.



“Anand Sumatilal Jain, Jayshree
M. Kundargi”, (2015), ‘Automatic number plate recognition using artificial
neural network’.


2.      “Jia.
J, H.Zhang and X.He”, (2007), ‘Region based license plate detection’, Journal
of Network Computer Applications.


3.      “Karthikeyan.V,
Vijayalakashmi V.J and Jeyakumar P”, (2013), ‘License plate segmentation using connected
component analysis’, IOSR Journal of Electronics and Communication Engineering.


4.      “Kethineni
Venkateswarlu and Sreerama Murthy Velaga”, (2015), ‘Text detection on scene
images using MSER’, International Journal of Research in Computer and
Communication Technology


5.      “Lim
H, Tay Y.M”, (2010), ‘Detection of license plate characters in natural scene
with MSER and SIFT unigram classifier’, IEEE Conference on Sustainable
Utilization and Development in Engineering and Technology’.


6.      “Rahman
C.A, W. Badawy and Radmanesh”,(2003), ‘A real time vehicle’s license plate
recognition system’ in Proceeding IEEE Conference on Advanced Video Signal
Based Surveillance.


7.      “Roshan
Dharshana, Yapaand Koichi Harada”, (2003), ‘Connected Component labelling
algorithms for grayscale images and evaluation of performance using digital






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