TRAFFIC CONTROL USING IMAGE PROCESSING

SUBMITTED BY

KAMRAN SHAHID BAIG

AMBER DEEP SINGH

ABDUL HADI

CONTENTS

1.Introduction2.TRAFFIC CONTROL USING IMAGE PROCESSING3.Block Diagram4.MATLAB5.Results6.Conlusion7.Future Scope8.References

INTRODUCTION

1. What is traffic control using image processing

2. How it differs from ordinary traffic control

3. Why Image processing

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

4.Matching

BLOCK DIAGRAM

IMAGE ACQISITION

IMAGE PRE-PROCESSING

1.Image rescaling or resizing

Robustness

2.RGB to Grey conversion

Colors does not matter for color blinds

Various algorithms

SimplestG=0.3R+0.59G+0.11BPercieved brightness is often dominated by green componentHuman Oriented

EDGE DETECTION

Various algorithms

• Sobel

• Prewit

• Roberts

• Log

• Canny

etc

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.

MATCHING

Matching is the most important step in various image

processing applications.

Pattern Vector

Matric defining pattern vectors

One example: Minimum distance

Euclidean distance

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.

GUI

>> Stands for Graphic

User Interface.

>> Programming very

difficult, however use

of GUIDE simplifies the

problem to greater

extent.

RESULTS

MATCHING 50-70% MATCHING 30-50%

RESULT CONTINUED

100% MATCH LESS THAN 30% MATCH

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.

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.

REFERENCES1. 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

THANK YOU