This document describes a system for traffic control using image processing. It begins with an introduction explaining traffic control using image processing and how it differs from ordinary traffic control. It then discusses the key steps in the image processing system, which include image acquisition, preprocessing such as resizing and color conversion, edge detection using algorithms like Canny, and pattern matching. It provides block diagrams and discusses using MATLAB and GUIs. Results show different levels of matching accuracy. It concludes that this method can remove problems like unnecessary green lights. Future work proposes a real-time system using DSP and vehicle identification.

1. Submitted by:
Chirag Panchal (1423001)
B.E.(EXTC)-A
K. J. Somaiya College of Engineering
TRAFFIC CONTROL USING
IMAGE PROCESSING

2. CONTENTS
1.Introduction
2.TRAFFIC CONTROL USING IMAGE PROCESSING
3.Block Diagram
4.MATLAB
5.Results
6.Conlusion
7.Future Scope
8.References

3. INTRODUCTION
1. What is traffic control using image processing
2. How it differs from ordinary traffic control
3. Why Image processing

6. TRAFFIC CONTROL USING IMAGE PROCESSING
Image Processing: Processing images using digital computers
1.Image Acquisition: Camera etc.
2.Image Pre-processing
Image Rescaling
RGB to Gray conversion
3.Edge Detection
Canny Algorithms
4.Matching

7. BLOCK DIAGRAM:-

8. IMAGE ACQISITION

9. IMAGE PRE-PROCESSING
1. Image rescaling or resizing
Robustness
2. RGB to Grey conversion
Colors does not matter for color blinds
3. Various algorithms
Simplest
G=0.3R + 0.59G + 0.11B
Perceived brightness is often dominated by green component
Human Oriented

10. EDGE DETECTION
Various Algorithms
• Sobel Algorithms
• Prewit Algorithms
• Roberts Algorithms
• Log Algorithms
• Canny Algorithms
etc.

11. CANNY Algorithms
Steps:-
1. Smooth the input with Gaussian filter.
2. Compute the gradient magnitude and angle images.
3. Apply nonmaxima suppression to the gradient
magnitude image.
4. Use double thresholding and connectivity analysis to
detect and link images.

12. MATCHING
Matching is the most important step in various image
processing applications.
Pattern Vector
Matric defining pattern vectors
One example: Minimum distance
Euclidean distance

13. MATLAB
1. Matrix Laboratories
2. It integrates computation, visualization, and programming
environment.
3. Exciting features
1. Simulink.
2. GUI
>> We have used GUIDE to make GUI.

14. GUI(Graphic User
Interface.)
>> Stands for Graphic User
Interface.
>> Programming very
difficult, however use of
GUIDE simplifies the
problem to greater extent.

15. RESULTS:-
Matching 50-70% Matching 30-50%

16. RESULT (Continued)
100% match Less than 30% match

17. CONCLUSION
Drawback of earlier methods
>> Wastage of time by lighting green signal even when road is empty.
Image processing removes such problem.
Slight difficult to implement in real time because the accuracy of time
calculation depends on relative position of camera.

18. FUTURE WORK
The focus shall be to implement the controller using DSP as it can avoid
heavy investment in industrial control computer while obtaining improved
computational power and optimized system structure. The hardware
implementation would enable the project to be used in real-time practical
conditions. In addition, we propose a system to identify the vehicles as they
pass by, giving preference to emergency vehicles and assisting in surveillance
on a large scale.

19. REFERENCES
1. Digital image processing by Rafael C. Gonzalez and Richard E.
Woods.
2. M. Siyal, and J. Ahmed, “A novel morphological edge detection
and window based approach for real-time road data control and
management,” Fifth IEEE Int. Conf. on Information,
Communications and Signal Processing, Bangkok, July 2005, pp.
324-328.
3. Y. Wu, F. Lian, and T. Chang, “Traffic monitoring and vehicle
tracking using roadside camera,” IEEE Int. Conf. on Robotics and
Automation, Taipei, Oct 2006, pp. 4631– 4636

20. THANK YOU…