Final Project presentation on Image processing based intelligent traffic control system+matlab gui
USING IMAGE PROCESSING
2.TRAFFIC CONTROL USING IMAGE PROCESSING
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
1.Image Acquisition: Camera etc
RGB to Gray conversion
1.Image rescaling or resizing
2.RGB to Grey conversion
Colors does not matter for color blinds
Percieved brightness is often dominated by green
1. Smooth the input with Gaussian filter.
2. Compute the gradient magnitude and angle
3. Apply nonmaxima suppression to the gradient
4. Use double thresholding and connectivity analysis
to detect and link images.
Matching is the most important step in various image
Matric defining pattern vectors
One example: Minimum distance
1. Matrix Laboratories
2. It integrates computation, visualization, and
3. Exciting features
>> We have used GUIDE to make GUI.
>> Stands for
however use of
GUIDE simplifies the
problem to greater
100% MATCH LESS THAN 30% MATCH
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.
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.
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