Final Project presentation on Image processing based intelligent traffic control system+matlab gui
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With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
• Smuggling of cars
• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
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The implementation of the project was done in the platforms of Matlab and OpenCV.
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1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
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3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
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either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
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developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
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parking space counter [Autosaved] (2).pptx![parking space counter [Autosaved] (2).pptx](https://cdn.slidesharecdn.com/ss_thumbnails/parkingspacecounterautosaved2-230903123620-8263d656-thumbnail.jpg?width=560&fit=bounds)
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With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example:
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• Invalid license plates
• Identification of stolen cars
• Usage of cars in terrorist attacks/illegal activities
In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime.
License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS).
Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition.
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1. ANPR uses optical character recognition on images captured by specialized cameras to read license plates on vehicles.
2. The cameras capture images that are then processed by ANPR software to detect, segment, and identify the license plate numbers.
3. ANPR systems are commonly used for electronic toll collection, traffic management, parking enforcement, and border control by storing images and license plate data.
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driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Image compression standards
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Pothole detection
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Final Project presentation on Image processing based intelligent traffic control system+matlab gui
- 1. TRAFFIC CONTROL USING IMAGE PROCESSING SUBMITTED BY KAMRAN SHAHID BAIG AMBER DEEP SINGH
- 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. INTRODUCTION 1. What is traffic control using image processing 2. How it differs from ordinary traffic control 3. Why Image processing
- 6. 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
- 9. 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
- 10. EDGE DETECTION Various algorithms • Sobel • Prewit • Roberts • Log • Canny etc
- 11. 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.
- 12. MATCHING Matching is the most important step in various image processing applications. Pattern Vector Matric defining pattern vectors One example: Minimum distance Euclidean distance
- 13. 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.
- 14. GUI >> Stands for Graphic User Interface. >> Programming very difficult, however use of GUIDE simplifies the problem to greater
- 15. RESULTS MATCHING 50-70% MATCHING 30-50%
- 16. RESULT CONTINUED 100% MATCH LESS THAN 30% MATCH
- 17. 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.
- 18. 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.
- 19. 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
- 20. THANK YOU