“ADVANCED calculating number of vehicle we are going to

 

“ADVANCED
TRAFFIC CONTROL MANAGEMENT SYSTEM”
Vimala Parangi, Deepti Dhadge, Poonam Kakade, Prof. Poonam Yewale
[email protected],[email protected]

Abstract: In this
paper we are going to propose a methodology for determining traffic congestion
on roads using image processing techniques and a model for controlling traffic
signals based on information received from images of vehicle present on the
roads taken by video camera. In a video frame instead of calculating number of
vehicle we are going to calculate the area occupied by the vehicle on the road
in the terms of pixels. Variable traffic cycle and weighted time these two
parameters are considered for each road based on density of vehicle and
sequence of control traffic lights.

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Keywords: Advance transportation system, traffic
light, image processing, edge detection, traffic density calculation.

 

 

 

 

I.                
INTRODUCTION1

Traffic congestion is when vehicle travel at slower speed because there
are more vehicle on than the road can handle this make trip times longer, and
increase queuing this is the major problem occur in day today life in big
cities. It is important to have a smart traffic control system to assure a safe
transportation. The very first step to do that is to acquire condition of
traffic i.e density of vehicles present on the road. From different sensors we
can take the information of traffic congestion. 
Induction loop, infra-red light sensor, optical flow etc these examples
of sensors.

 In day to day life image
processing techniques 14 has been very important and promising topic to deal
with traffic related problems because of its ease of maintenance and being more
smart as well as intelligent system. Different methods 2-5 have been
proposed to acquire traffic information. Most of the work detects edge of the
vehicles and counts the number of vehicle present on the road. However the
disadvantage of the method is that counting the number of vehicles may give
wrong results when spaces between the vehicles on the road are very small (i.e.
two cars very close to each other may be counted as one vehicle).

In this paper we are going to propose a methodology for determining
traffic congestion on roads using image processing techniques and a model for
controlling traffic signals based on information received from images of
vehicle present on the roads taken by video camera. In a video frame instead of
calculating number of vehicle we are going to calculate the area occupied by
the vehicle on the road in the terms of pixels. Variable traffic cycle and
weighted time these two parameters are considered for each road based on
density of vehicle and sequence of control traffic lights.

 

 

 

 

II. LITERATURE SURVEY

Md. Munir Hasan, Gobinda Saha, Aminul Hoque and Md. Badruddoja Majumde ,
In this paper they propose a method for determining traffic congestion on roads
using image processing techniques and a model for controlling traffic signals
based on information received from images of roads taken by video camera. They
extract traffic density which corresponds to total area occupied by vehicles on
the road in terms of total amount of pixels in a video frame instead of
calculating number of vehicles. They set two parameters as output,variable
traffic cycle and weighted time for each road based on traffic density and
control traffic lights in a sequential manner.

  

Prashant Jadhav, Pratiksha
Kelkar, Kunal Patil, and Snehal Thorat ,The fact is that, the population of city and
numbers of vehicles on the road are increasing day by day. With increasing
urban population and hence the number of vehicles, need of controlling streets,
highways and roads is major issue. The main reason behind today’s traffic
problem is the techniques that are used for traffic management. Today’s traffic
management system has no emphasis on live traffic scenario, which leads to
inefficient traffic management systems. This project has been implemented by
using the Mat lab software and it aims to prevent heavy traffic congestion.
Moreover, for implementing this project Image processing technique is used.

                                                                                       

Vismay Pandit, Jinesh Doshi,
Dhruv Mehta, Ashay Mhatre and Abhilash Janardhan, The simplest way for  controlling a traffic light uses timer for each
phase. Another way is to use.Electronic sensors in order to detect vehicles,
and produce signal that cycles. We propose a system for controlling the traffic
light by image processing. The system will detect

Vehicles through
images instead of using electronic sensors embedded in the pavement. A camera
will be installed alongside the traffic light.

 

Omkar Ramdas Gaikwad, Anil Vishwasrao, Prof. Kanchan
Pujari, Tejas Talathi

 The main reason behind today’s traffic problem is the techniques that are
used for traffic management. Today’s traffic management system has no emphasis
on live traffic scenario, which leads to inefficient traffic management
systems. These traffic timers just show the preset time. This is like using
open loop system. If we incorporate a closed loop system using camera, it is
possible to predict the exact time on traffic light timers. If the traffic
light timers are showing correct time to regulate the traffic, then the time
wasted on unwanted green signals (green signal, when there is no traffic) will
be saved. Timer for every lane is the simplest way to control traffic. And if
those timers are predicting exact time then automatically the system will be
more efficient. This paper represents the project that has been implemented by
using the Matlab software and it aims to prevent heavy traffic congestion.  This
project does not actually measure the number of vehicles present on the road,
but measures the area covered by vehicles on the road

 A web camera is placed in a traffic lane that
will capture images of the road on which we want to control traffic. Then these
images are efficiently processed to know the traffic density. According to the
processed data from Matlab, the controller will send the command to the timer
to show particular time on the signal to manage traffic.

III.
METHDOLOGY

 

A)    
PRE-PROCESSING:

 Pre-processing is a technique used to convert
RGB color to gray color image. It is done by using luminance converter                        shown in below
equation.

Is=0.2896*IR+0.5870*IG+0.1140*IB      

 
Is the grey level image IR, IG, IB   are the luminance in red, luminance in green
and luminance in blue.

LCD 16X2
 
 
 

 

 

          

16*2 LCD

 

                 

Camera
 
 
 

 

LED Panel 1

 

 

 

 

 

                                                    

LED Panel 1

 

 

   Computer
(with MATLAB 
installed )

 

                                    

 

 

 

 

 

 

 

                     
Figure 1:  Block Diagram

 

B)    
IMAGE ENHANCEMENT:

 

 Better contrast and
detailed image are provided by enhancing an image compare to a non enhanced
one. Some of image enhancement techniques are power-law

Transformation, linear method and Logarithmic method.  Among them, power law transformation method
is best approach which has the basic formula as shown below:                               V = K v? Where V
and v are I/O gray levels, ? & K is a

positive constant (K=1). Therefore, deciding an accurate utility
of ? can play a pretentious action in image heighten process. For attain a
Gamma correction, the association.

Between light
input and output signals must be taken. This is done by the following equation

                        S (0) =K. (e) (E)

S (0) = K. (e)
(E) is output gain and K is the exposure time that is related to intensity and
linear vehicles.

 

C)    
OBJECT DETECTION:

 

  Edges of an image correspond to object
boundaries. These edges are nothing but pixels where the change in brightness
may occur and is calculated the behavior of image function in a neighboring
pixel. 

 

D)    
EDGE DETECTION:

 

      It is an image processing
technique for finding the boundaries of object within image. In the detection unit we

Detect the edges of image in to
two parts that is background and foreground image

1)    After edge detection we
get two images foreground and background image, by subtracting two images we
get foreground object and background object 13.

2)    To reduce the additive
noise and blurring effect which added by the processing of subtraction we use
wiener filter.

 

E)    
MORPHOLOGICAL OPERATION:

 

        There are two types
of morphological operation first is      
morphological opening and morphological closing. In this project we
perform morphological image closing to remove small holes within an image.

1)     To
fill the holes in the objects with closed contours we perform flood fill operation
6. We get solid foreground image.

2)     Now
we convert gray scale image to binary image.

 

                              
Figure 2:  Street intersection

 

IV. CONCLUSION

 

             In this paper, the image captured
by the camera from the road and after that captured videos are arranged in
serial image. The number of cars has been counted by processing on the each
captured image. By setting the threshold value the number of cars exceeds the
threshold value the heavy traffic will be shown automatically. The advantages
of this new method include such as use of image processing over sensors, low
cost, easy setup and relatively good accuracy and speed. Because this method
has been implemented using Image Processing and Matlab software, production costs
are low while achieving high speed and accuracy.

 

REFERENCES

1 Md.Munir Hasan, Gobinda Saha and Md. Badruddoja Majumder”
Smart Traffic Control System with Application of Image Processing Techniques”
Publish in 3rd International
Conference on Informatics, Electronics & vision 2014.

 

2 D. Beymer, P. McLauchlan, B. Coifman, and J. Malik, “A
real-time computer vision system for measuring traf?c parameters,” IEEE Conf.
on Computer Vision and Pattern Recognition, pp. 495-501, 1997.

 

 3 M. Fathy, and M. Y.
Siyal, “An image detection technique based on morphological edge detection and
background differencing for realtime traf?c analysis,” Pattern Recognition
Letters, vol. 16, pp. 1321-1330, Dec. 1995.

 

 4 R. Cucchiara, M.
Piccardi, and P. Mello, “Image analysis and rule-based reasoning for a traf?c
monitoring system,” IEEE Trans. on Intelligent Transportation Systems, Vol. 1,
Issue 2, pp 119-130, 2000.

 

5 P. Choudekar, A. K. Garg, S. Banerjee, M. K. Muju,
“Implementation of image processing in real time traf?c light control,” IEEE
Conf. on Electronics Computer Technology, Vol. 2, pp. 94-98, 2011.

 

6 P. Soille, Morphological Image Analysis:
Principles and Applications, Springer-Verlag, 1999, pp. 173-174.

 

7 Prashant
Jadhav, Pratiksha Kelkar, Kunal Patil, Snehal Thorat” Smart Traffic
Control System Using Image Processing” Publish in International Research
Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 03 | Mar-2016

 

8 Omkar
Ramdas Gaikwad, Anil Vishwasrao, Prof. Kanchan Pujari, Tejas Talathi,” Image
Processing Based Traffic Light Control” International Journal of Science,
Engineering and Technology Research (IJSETR) Volume 3, Issue 4, April 2014

 

9Vismay Pandit, Jinesh Doshi, Dhruv Mehta, Ashay
Mhatre and Abhilash Janardhan.” Smart Traffic Control System Using Image
Processing” Publish in International Journal of Emerging Trends & Technology in Computer
Science (IJETTCS) Volume 3, Issue 1, January February
2014

 

 10 Image Processing Based Traffic Light
Control

Omkar
Gaikwad, Anil Vishwasrao, Prof. Kanchan Pujari, Tejas Talathi International
Journal of Science, Engineering and Technology Research (IJSETR) Volume 3,
Issue 4, April 2014

 

11
intelligent traffic light controller using embedded system Sayali Ambekar,
Shraddha Jawalkar, Anagha Patil, Shweta Patil International Research Journal of
Engineering and Technology (IRJET) Volume: 04 Issue: 02 | Feb -2017

 

12
Smart Traffic Lights Switching and Traffic Density Calculation using Video
Processing 

 Anurag Kanungo,Ayush Sharma, Chetan
Singla  Proceedings of 2014 RAECS UIET
Panjab University Chandigarh, 06 – 08 March, 2014

 

13
M. Piccardi, “Background subtraction techniques: a review,” IEEE International
Conference on Systems, Man and Cybernetics 4, pp. 30993104, Oct. 2004.

 

14V.
Kastrinaki, M. Zervakis, and K. Kalaitzakis, “A survey of video processing techniques   applications”, Image and Vision Computing,
vol. 21, pp. 359-381, Apr 1 2003.