finding parking availability for a specific time period is a very tedious job in urban areas. The Indian government now focusing on t he smart city project, already they published city name for a n upcoming smart city project. In smart city application , intelligent transportation system (ITS) plays an important role- in that finding parking place, specifically for the car owner to avoid time computation, as well as congestion in traffic is going to be very important. In this article, we propose an intelligent car parking system for the smart city using Circle Hough Transform (CHT).

1. International Conference on Energy, Communication, Data Analytics and Soft Computing (lCECDS-20 17)
OpenCVand Matlab based Car Parking System
Module for Smart City using Circle Hough Transform
Janak Trivedi" , Dr. Mandalapu Sarada Devi *2, Dave Dhara *3
# Asst. Prof - Electronics & Communication Department- G.£. C. Bhavnagar, G.T U; "Principe]. - A.I. T , Ahmedabad, G.T U;
'Asst. Prof - Electronics & Communication Department- G.£. C. Bhavnagar
Gujarat, India
It r i ve d i janak2611@yahoo .com
3 saradadeviml@gmail .com
3 dave . dhara24888&gmail . com
Abstract--- finding parking avai lability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on the smart city project, already they pub lished city
name for an upcoming smart city project. In smart city
app lication , intelligent transportatlon system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car pa rk ing system for the sma rt city using Circle
Hough Transform (CHT).
Keywords- Intelligent transportation system (ITS), Circle Hough
Transform (CHT), Circle detection, Video-Image processing,
smart city, pa rking system , OpenCV.
I. INTRODUCTION
For today' s traffic monitoring and its management is a
recent trend in research development. In this paper, we are
focusing on parking component of the traffic parameter.
Traffic very congested from last decade due to the increasing
rise of automobile companies offers to a customer,
privatization of that- mainly more and more used in present
day compared to last decade and it's also increasing in the
future may be with same or more speed. So now government
thinking how to solve this problem in real time? Within
specified time duration.
In this paper, in the second section, first we disused how
it's necessary - Parking slot arrangement in a smart city, in
next section deal with different method already demonstrated
or discussed in the earlier paper, in the fourth section simply
demonstrates how Generalized Hough-Transform and circle
Hough transform useful for detecting circle. Next section
simulation result of a different image available from google
and capture from the camera for circle shape detection from
Fig. [I] - [8] And fig. [9] Demonstrate the simulation result of
the parking strategy in real time, and lastly discussed future
scope and conclusion of this work.
II. PA RKING SLOT NECE SSITY
Nowadays increasing a rate of buying personal vehicle
rapidly increase. In particularly four wheeler vehicles
successfully doing their business, so in that scenario think
about that, if there is no place for parking or limited parking
978-1-5386-1887-51171$31.00 ©2017 IEEE
without any parking management get congestion and due to
that time and energy consumption more and more. To avoid
these situations in the near future, a city trying to develop as
the smart city, Parking slot may be played very important role.
In this article, we try to demonstrate Parking slot for
particularly Four wheelers, Hough transform for shape
detection . Before that discussion about how different methods
were used in the past. Then get simulation result with Matlab
software for circle detection using Hough transform then
trying to get similar results using OpenCY plus Python
software.
Procedure
Personal Computer (P.C) or Laptop used as system for
Image analysis, acquire image/video from web camera or
USB camera. Then from those images finding circle using
circle Hough transform, or using Hough transform with the
help of Matlab and Python/OpenCY software.
III. D IFFERENT METHODS DISCUSSED IN EARLIER PAPERS
In [I] circle Hough transform (CHT), modification from
simple or generalized Hough transforms (HT) to find a circle
from grayscale image discussed. The voting procedure is used
for edge detection. A method implemented in real time
application platform like sports.
In another paper [2] first use neural network for edge
detection then Hough transforms used for specific line
detection. Where as in [3] Modified Hough transform (MHT)
is used for circle detection. First, use Sobel operator for edge
detection then MHT used.
Partial Least square regression (PLS) analysis [4] [5] for
vehicle detection and feature selection for the same using
Hough transform discussed. [6] Hough transform of the line,
Hough transform of the circle, implementation with
Mathematical expression. In [7] concentric circle detection
method using Hough transform discussed. In the same article
radius detection with the help of I-D HT implemented.
Incremental circle Hough transforms [8] used for shape
recognition for reduced computation cost of the voting
procedure and also useful for hardware implementation. [9]
Article discussed how half and full helmet wearing detection
possible in Thailand uses circle Hough transform. This
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2. International Conference on Energy, Communication, Data Analytics and Soft Computing (lCECDS-20 17)
method is very useful in real time traffic management. The
similar way another traffic management problem can be
solved by the Hough transform [10] for traffic light detection
and recognition. Use of Mexican hat filter in addition of HT
[II] useful to concentrate the peaks of Hough local maxima.
IV. HOUGH TRANSFORMS
Generalized Hough Transform (GHT) uses the parametric
representation of a line:
rho = x*cos(theta) + y*sin(theta) ------------------------( I)
In equation (I) variable rho is the distance from the origin to
the line along a vector perpendicular to the line. theta is the
angle of the perpendicular projection from the origin to the
line measured in degrees clockwise from the positive x-axis.
The range of theta is - 90 0 ~ 8 < 90 ° . The angle of the line itself
is 8+90°, also measured clockwise with respect to the positive
x-axis. (From Matlab Help)
A circle is represented mathematically as,
( _. )2+ (y _ y )2 _ ,,2• • C 111 r C 111 r - --------(2)
Where (. C Il ler : Yc 111 r)is the center of the circle, and or is
the radius of the circle. From the equation, we can see, we
have 3 parameters, so we need a 3D accumulator for the
Hough transform, which would be highly ineffective. So
OpenCV uses a trickier method, Ho ugh G radient Method,
which uses the gradient information of edges. (From OpenCV
Python Help)
V. SIMULATION RESULT
the circle from different sources of imagery, collected, then
apply GHT and CHT for shape detection in the same . Fig. [I ]
And [2] shows the different radius of circle detected from
images collected randomly from google using Matlab.
Actually, our aim of this shape - circle, detection, and
recognition for Parking spot checking, which has currently
manually operated in the city. Here specific threshold used for
detection of the accurate result. Here as shown in different
simulation result, circle detection is done in a directly
available image or it can also be done using first convert any
image to grayscale then CHT or GHT can be applied. Fig. (4)
One out of 5 caps is not detected, so here illumination gets a
very important role in any computer vision related work,
trying to minimi ze as early as possible. Fig. [I ] - [4] For
Matlab based simulation result, whereas fig. [5] - [8] For
OpenCV Python based simulation result.
!1h;lJtZ ~_ : -:::l. ~~-..-..-_-_-.-----=~ !dl r.. ;"rt Toe. ~ ...... HI, ~ ~ j " !I.,.111 I;
~ ~ ~ /. ·1'I 1 0 !l •
I
Fig. (2) Circle detection in google image processing using
Matlab.
Help ." l'JFigur. 3
File Edit View Insert Tools Desktop Window Help
I'J Figu_r._l ....@[ID ~
File Edit View Insert Tools Desktop Window
6 a L
Fig. (I) Circle detection in google image processing using
Matlab.
Now from the above discussion, a first different radius of
Fig. (3) Real time circle shape detection using GHT using
Matlab.
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3. International Conference on Energy, Communication, Data Analytics and Soft Computing (lCECDS-20 17)
,.,.---------: ~ _ P...------- """l'1 ~ OpenCY python.
Fig. (4) Real time circle shape detection using GHT using
Matlab.
Fig. [3] And [4] give the result of the real time circle
detection. In fig. [3] Different radius of the circle made and
then detected same, in fig. [4] Coin and different bottle cap
(Like Pepsi, coca cola, mineral water etc..) detected as circle
shape.
Fig. (7) Real time circle shape detection using CHT using
OpenCv python.
Fig. (5) Circle detection in google image processing using
OpenCY Python.
Fig. (8) Real image processing using OpenCY python.
IT (:)rtours U ,'>( I
In Fig. [5], [6], [7], [8] circle shape detection using CHT using
OpenCY Python, this is same image fig. [2], [3], [4] which is
used for circle shape detection using GHT using Matlab, as
shown here.
•
•
•••••
-,I
••
Fig. (9) Real time circle detection from video using OpenCY
python.
••
•
••
••
•
• ' detected circles
Fig. (6) Real time circle shape detection using CHT using
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4. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-20 17)
Fig. [9] Represent demonstration for a parking slot
with four wheeler toy vehicle. In shown fig. Within road side
car parking spot darken with a circle, now if any car or four
wheeler vehicle get that spot, then, that particular circle block
and not detected that part of parking slots. In ITS parking
system play vital role to minimize traffic congestion as well as
saving time is a big factor and this can be achieved
automaticall y using video- image processing.
VI. CONCLUSION AND FUTUR E WORK
As shown in simulation result fig. [I] - [8] For circle
shape detection using Matlab and OpenCV python, and Fig. [9]
Real time detection from the video where a camera is used to
capture that video, for parking slot detection is very useful for
future work as automatic parking slot detection in a smart city,
where time and energy both can save using a focus on the
Parking Deposit system. From this work as a future work
trying to develop Graphical User Interface based simulation so
get parking slot availability with the help of the message.
Develop algorithms with minimum false positive, all
correct detection of specific shapes, with minimum time. As
shown in above simulation result compare fig. (4) And (7),
Fig. (4) Where lout of 5 caps not detected, whereas in fig. (7)
That can be detected using OpenCV Python. So trying to
reduce all this error as minimum as possible in future work.
A CKNOWL EDGM ENT
In this article, Simulation result develops using Matlab and
OpenCV Python software.
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