Final Project presentation on Image processing based intelligent traffic control system+matlab gui
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Transcript of Final Project presentation on Image processing based intelligent traffic control system+matlab gui
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TRAFFIC CONTROL USING IMAGE PROCESSING
SUBMITTED BY
KAMRAN SHAHID BAIG
AMBER DEEP SINGH
ABDUL HADI
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CONTENTS
1.Introduction2.TRAFFIC CONTROL USING IMAGE PROCESSING3.Block Diagram4.MATLAB5.Results6.Conlusion7.Future Scope8.References
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INTRODUCTION
1. What is traffic control using image processing
2. How it differs from ordinary traffic control
3. Why Image processing
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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
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BLOCK DIAGRAM
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IMAGE ACQISITION
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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
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EDGE DETECTION
Various algorithms
• Sobel
• Prewit
• Roberts
• Log
• Canny
etc
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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.
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MATCHING
Matching is the most important step in various image
processing applications.
Pattern Vector
Matric defining pattern vectors
One example: Minimum distance
Euclidean distance
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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.
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GUI
>> Stands for Graphic
User Interface.
>> Programming very
difficult, however use
of GUIDE simplifies the
problem to greater
extent.
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RESULTS
MATCHING 50-70% MATCHING 30-50%
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RESULT CONTINUED
100% MATCH LESS THAN 30% MATCH
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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.
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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.
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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
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THANK YOU