application of ip techniques in traffic control system

APPLICATION OF IP

TECHNIQUES IN TRAFFIC

CONTROL SYSTEM

PIXEL DENSITY

METHODASHIK.S.R

[email protected]

Electronics Engineering

Central Polytechnic College

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Objectives and scopes

Traffic congestion is becoming more serious day after day.

Trying to find out a technique for determining traffic congestion on roads using

image processing techniques.

This method will reduce the necessity of intense man power for traffic control

and wastage of green light on empty roads.


Introduction

Image processing is an efficient tool for overcoming traffic problems.

Image processing techniques can be used to find out the density of traffic on

roads.

Proposes a method to find out the traffic density on roads using image

subtraction and segmentation.

This is a method of finding traffic density in terms of total amount of pixels in a

video frame instead of calculating number of vehicles using image processing

techniques.


Traffic Control Using Image

Processing

Image processing is simply processing images using digital computers.

Steps involved here are

• Video acquisition using camera.

• Image pre-processing .

• RGB to gray conversion.

• Edge detection

• Sobel operation.

• Image subtraction.

• Filtering

• Weiner filter

• Image post-processing

• Morphological closing & flood fill operation

• Thresholding

• converting grayscale image to binary.


Camera video

stream

RGB foreground image (FGrgb) RGB background image (BGrgb)

RGB to gray conversion (FGgray) RGB to gray conversion (BGgray)

Edge

detection

(FGp)

Edge

detection

(BGp)

Image subtraction & enhancement

Binary image(Gbinary)

Direct subtraction

Dobj=FGgray-BGgray

Image enhancement

Binary image(Dbinary)

Itotal

Block Diagram


First a video camera is used for capturing image.

From the camera video stream data is processed frame by frame.

The empty road will be the background image and subsequent frames from

video camera will be the foreground image.

Background image is taken as the reference image.

VIDEO

ACQUISITION

GRADIENT MAGNITUDE METHOD


IMAGE PRE-

PROCESSING RGB foreground image(FGrgb) and background image(BGrgb) are

converted grayscale image (FGgray & BGgray)

Various algorithms are there. The simplest one is

I=0.33*R+0.33*G+0.33*B

R,G,B:- red, green,blue value of each pixel.


EDGE DETECTION

Sobel edge detecting operation is performed on foreground and

background image.

It measures 2-D gradient measurement using horizontal and vertical

gradient.

Horizontal gradient

Vertical gradient


IMAGE SUBTRACTION

Our aim is to extract the foreground objects(ie, vehicles) from the

background.

Edge detected foreground and background images are subtracted.

Gobj= FGp-BGp

Then we get foreground objects(ie, vehicles)


FILTERING

Noise removal to remove the noise introduced by subtraction.

Wiener filter is used because of its ability to remove the additive noise and

invert the blurring simultaneously.

Before we perform filtering we try to reduce small intensity pixels by

subtracting a fixed small value.


IMAGE POST-

PROCESSING Morphological image closing

Essentially performs dilation followed by erosion.

This procedure helps us to construct the edges

found by sobel operation

Flood fill operation

To fill holes in the objects with closed contours with

solid foreground objects


THRESHOLDING

We obtain a binary image by thresholding.

We apply Otsu’s method to obtain the threshold T needed to

convert grayscale image to binary.

To enhance the binary image we multiply the threshold by a factor.


Direct Subtraction

• The grayscale background is subtracted from gray scale foreground to get

Dobj where foreground objects are visible.

Dobj = FGgray-BGgray

• Then perform the above said image enhancement steps and thesholding to

get the binary image Dbinary.


Using Both methods Together

The binary images Gbinary & Dbinary are added to get the final image.

Itotal = Gbinary +Dbinary

Itotal = 1 if pixel value>=1

0 else

The amount of white pixels in Itotal represents the foreground objects.


Traffic Density Calculation

Traffic density is given by

Where R & C is the number of rows and columns in Itotal.


The traffic cycle is taken as a function of total traffic density (TD) of

vehicles.

ie, Tc = f(TD)

The denser the traffic, longer is the traffic cycle.

Another parameter is weighted time allocation of vehicles.


Our main target is to pass traffic from the road with the higher density. For

this reason, a weighted time allocation is chosen.

All the computations described here can be implemented in Matlab.

The Matlab sends necessary information to the microcontroller for

particular signal to be lighted.


Why two methods are used for finding final image ?

In direct subtraction method, if the vehicle colour is black it may not be

detected. This problem is solved by the gradient magnitude method

where vehicle colour is not a factor.

In gradient magnitude method there can be certain situations where

detected edges may not form closed contour. This problem can be

solved by background subtraction.

Using this traffic density information we can calculate traffic cycle (Tc)

which is the total time required for one complete rotation of the signal

lights at any traffic point.


Conclusion Automatic traffic control system using timers which is used earlier

had a drawback that the time is being wasted by green light on the

empty road. This technique avoids this problem.

Here we have successfully implemented an algorithm for a real time

image processing based traffic controller.

Image processing is a far more efficient method of traffic control as

compared to traditional techniques.

Also image processing methods are easy to implement and are cost

effective.


Future Scope

We can use satellite images instead of video cameras

In addition, we can propose a system to identify the vehicles as

they pass by, giving preference to emergency vehicles and

assisting in surveillance on a large scale.


References1)V. Kastrinaki, M. Zervakis, and K. Kalaitzakis, “A survey of video processing

techniques for traffic applications,” Image and Vision Computing, vol. 21, pp.

359-381, Apr 1 2003.

2)D. Beymer, P. McLauchlan, B. Coifman, and J. Malik, “A real-time computer

vision system for measuring traffic 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 traffic

analysis,” Pattern Recognition Letters, vol. 16, pp. 1321-1330, Dec. 1995.

4) M. Piccardi, “Background subtraction techniques: a review,” IEEE

International Conference on Systems, Man and Cybernetics 4, pp. 3099-

3104, Oct. 2004.

5) R. Cucchiara, M. Piccardi, and P. Mello, “Image analysis and rule-based

reasoning for a traffic monitoring system,” IEEE Trans. on Intelligent

Transportation Systems, Vol. 1, Issue 2, pp 119-130, 2000


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