Traffic control using image processing

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

CONTENTS

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

INTRODUCTION

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 computers1.Image Acquisition: Camera etc.2.Image Pre-processing Image Rescaling RGB to Gray conversion3.Edge Detection Canny Algorithms4.Matching

BLOCK DIAGRAM:-

IMAGE ACQISITION

IMAGE PRE-PROCESSING1. Image rescaling or resizing Robustness2. RGB to Grey conversion Colors does not matter for color blinds 3. Various algorithmsSimplestG=0.3R + 0.59G + 0.11BPerceived brightness is often dominated by green componentHuman Oriented

EDGE DETECTIONVarious AlgorithmsSobel AlgorithmsPrewit AlgorithmsRoberts AlgorithmsLog AlgorithmsCanny Algorithms etc.

CANNY AlgorithmsSteps:-Smooth the input with Gaussian filter.Compute the gradient magnitude and angle images.Apply nonmaxima suppression to the gradient magnitude image.Use double thresholding and connectivity analysis to detect and link images.

MATCHINGMatching is the most important step in various image processing applications.Pattern VectorMatric defining pattern vectorsOne example: Minimum distanceEuclidean distance

MATLABMatrix LaboratoriesIt integrates computation, visualization, and programming environment.Exciting features 1. Simulink. 2. GUI>> We have used GUIDE to make GUI.

GUI(Graphic User Interface.)>> 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% matchLess 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.

REFERENCES

Digital image processing by Rafael C. Gonzalez and Richard E. Woods.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.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