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Final Project presentation on Image processing based intelligent traffic control system+matlab gui

This is final project presentation on Image processing based intelligent traffic control system+matlab gui.

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Final Project presentation on Image processing based intelligent traffic control system+matlab gui

  1. 1. TRAFFIC CONTROL USING IMAGE PROCESSING SUBMITTED BY KAMRAN SHAHID BAIG AMBER DEEP SINGH
  2. 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. 3. INTRODUCTION 1. What is traffic control using image processing 2. How it differs from ordinary traffic control 3. Why Image processing
  4. 4. 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
  5. 5. BLOCK DIAGRAM
  6. 6. IMAGE ACQISITION
  7. 7. 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
  8. 8. EDGE DETECTION Various algorithms • Sobel • Prewit • Roberts • Log • Canny etc
  9. 9. 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.
  10. 10. MATCHING Matching is the most important step in various image processing applications. Pattern Vector Matric defining pattern vectors One example: Minimum distance Euclidean distance
  11. 11. 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.
  12. 12. GUI >> Stands for Graphic User Interface. >> Programming very difficult, however use of GUIDE simplifies the problem to greater
  13. 13. RESULTS MATCHING 50-70% MATCHING 30-50%
  14. 14. RESULT CONTINUED 100% MATCH LESS THAN 30% MATCH
  15. 15. 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.
  16. 16. 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.
  17. 17. 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
  18. 18. THANK YOU

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