The document discusses the implementation of traffic control using image processing, highlighting its advantages over traditional methods such as reducing unnecessary signal changes. It outlines key processes including image acquisition, pre-processing, edge detection, and matching algorithms, as well as the use of MATLAB for system design. Future enhancements aim to integrate DSP for real-time applications and improve vehicle identification systems.

1. TRAFFIC CONTROL
USING IMAGE PROCESSING
SUBMITTED BY
KAMRAN
SHAHID BAIG
AMBER DEEP
SINGH

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

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
Various algorithms
Simplest
G=0.3R+0.59G+0.11B
Percieved brightness is often dominated by green
component
Human Oriented

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

11. CANNY
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
>> Stands for
Graphic User
Interface.
>> Programming
very difficult,
however use of
GUIDE simplifies the
problem to greater

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