This document discusses a method for traffic congestion detection using image processing techniques, aiming to reduce manpower and waste of resources. The proposed technique utilizes video analysis to assess traffic density by comparing captured images and involves several processing steps including edge detection, filtering, and image enhancement. A more efficient automatic traffic control system is presented, which could potentially employ satellite imagery and vehicle identification for improved traffic management.

1. APPLICATION OF IP
TECHNIQUES IN TRAFFIC
CONTROL SYSTEM
PIXEL DENSITY
METHOD ASHIK.S.R
ashikask@live.com
Electronics Engineering
Central Polytechnic College
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2. 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.
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3. 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.
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4. 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.
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5. 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
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6.  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
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7. 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.
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8. 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
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9. 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)
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10. 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.
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11. 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
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12. 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.
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13. 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.
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14. 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.
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15. Traffic Density Calculation
 Traffic density is given by
Where R & C is the number of rows and columns in Itotal.
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16.  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.
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17.  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.
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18. 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.
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23. 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.
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24. 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.
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25. References
1)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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