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On-Board License Plate Detection and Recognition
Tomovič, Martin ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This Bachelor's thesis aims to create an aplication for detection and recognition of license plates suitable for real-time processing. The work contains analysis of available methods. Part of the work is focused on present form of licence plates in Czech Republic. As a result of work, new data set was created and computer application was implemented. The application uses existing libraries designed for computer vision and machine learning with main purpose to detect and recognize licence plates from video. Detection is realized with help of cascade classifier, and recognition by Perceptron neural network. Final chapter subsequently contains evaluation of success rate of implemented solution.
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License Plate Detection and Recognition from Still Image
Janíček, Kryštof ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This thesis describes the design and implementation of system for detection and recognition of license plate. This system is divided into three parts which are license plate detection, character segmentation and optical character recognition. License plate detection is done by cascade classifier that achieves hit rate of 95.5% and precision rate of 95.9%. Character segmentation is based on contour finding that achieves hit rate of 93.3% and precision rate of 96.5%. Optical character recognition is done by neural network and achieves hit rate of 98.4% for individual characters. The whole system is able to detect and recognize up to 81.5% of license plates from the test data set.
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Detection, Tracking and Classification of Vehicles
Vopálenský, Radek ; Sochor, Jakub (referee) ; Juránek, Roman (advisor)
The aim of this master thesis is to design and implement a system for the detection, tracking and classification of vehicles from streams or records from traffic cameras in language C++. The system runs on the platform Robot Operating System and uses the OpenCV, FFmpeg, TensorFlow and Keras libraries. For detection cascade classifier is used, for tracking Kalman filter and for classification of the convolutional neural network. Out of a total of 627 cars, 479 were tracked correctly. From this number 458 were classified (trucks or lorries not included). The resulting system can be used for traffic analysis.
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Detection, Tracking and Classification of Vehicles
Vopálenský, Radek ; Herout, Adam (referee) ; Juránek, Roman (advisor)
The aim of this master thesis is to design and implementation in language C++ a system for the detection, tracking and classification of vehicles from streams or records from traffic cameras. The system runs on the platform Robot Operating System and uses the OpenCV, FFmpeg, TensorFlow and Keras libraries. For detection is used cascade classifier, for tracking Kalman filter and for classification of the convolutional neural network. Success rate for detection is 91.93 %, tracking 81.94 % and classification 63.72 %. This system is part of a comprehensive system, that can moreover calibrate video and measure of vehicles speed. The resulting system can be used for traffic analysis.
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Detection, Tracking and Classification of Vehicles
Vopálenský, Radek ; Sochor, Jakub (referee) ; Juránek, Roman (advisor)
The aim of this master thesis is to design and implement a system for the detection, tracking and classification of vehicles from streams or records from traffic cameras in language C++. The system runs on the platform Robot Operating System and uses the OpenCV, FFmpeg, TensorFlow and Keras libraries. For detection cascade classifier is used, for tracking Kalman filter and for classification of the convolutional neural network. Out of a total of 627 cars, 479 were tracked correctly. From this number 458 were classified (trucks or lorries not included). The resulting system can be used for traffic analysis.
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Captcha Code Recognition
Pazderka, Radek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor thesis is dedicated to design and implementation of application , which's purpose is to recognize text CAPTCHA codes . It describes image processing algorithms , segmentation algorithms and character classification . Two different aproaches were used for classification . Convolution neural network LeNet and histogram classificator , which uses Pearson's correlation coefficient . Chosen classificators were tested on different CAPTCHA codes while finding out the success rate of recognition .
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Vehicle On-Board Camera Analysis
Kadeřábek, Jan ; Bartl, Vojtěch (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on analysis of video from vehicle on-board camera. During the process of analysis, probihibitory traffic signs are detected and their specific type is classified. For recognized speed limit signs, their numeric value is extracted. From the processed information, it will try to create a file containing the unique occurrences of traffic signs including their GPS coordinates. For the purpose of detection and recognition of traffic signs, several data sets were created. A~cascade classifier with LBP features is used as a detector. Classification of the type and value of traffic signs is done using the k-Nearest Neigbour method.
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Vehicle Following Distance Estimation from Mobile Phone in Vehicle
Zemánek, Ondřej ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
The aim of this bachelor thesis is to create a mobile application for Android that estimates the distance of vehicles based on vehicle size in the camera image of the mobile phone. The estimation of the following distance is evaluated based on known camera parameters, the average vehicle width and the size of image area that represents detected car. Vehicles and their licences plates are detected in the image using cascade classifiers. Licence plate is detected only in the area of the detected vehicle. A training dataset for cascade classifier was created as part of this work. The cascade classifier is designed for vehicle detection. This work is extended with feature that tracks following distance in time and warns you with an acoustic signal on sudden distances change. This thesis is divided into five main parts - comparison of existing solutions for distance estimation, review of object detection methods, application design, implementation and evaluation of detectors, distance evaluation.
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Re-Identification of Vehicles by License Plate Recognition
Špaňhel, Jakub ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.
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License Plate Detection and Recognition from Still Image
Janíček, Kryštof ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This thesis describes the design and implementation of system for detection and recognition of license plate. This system is divided into three parts which are license plate detection, character segmentation and optical character recognition. License plate detection is done by cascade classifier that achieves hit rate of 95.5% and precision rate of 95.9%. Character segmentation is based on contour finding that achieves hit rate of 93.3% and precision rate of 96.5%. Optical character recognition is done by neural network and achieves hit rate of 98.4% for individual characters. The whole system is able to detect and recognize up to 81.5% of license plates from the test data set.
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