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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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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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Vehicle Speed Measurement by a Stationary Camera
Juřica, Tomáš ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
This Bachelor's thesis deals with the problematic of car speed measurement from video footage captured by a stationary camera. Development of a tool focused on reaching maximum accuracy of measurements with minimal user effort has been covered in this work. Perception of scene dimensions is acquired by using known points in the scene, which are manually marked. The influence of the way of annotating car position and input video quality on maximal reachable accuracy has also been discussed in this work.
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Vehicles Re-Identification with License Plate
Hažmuka, Tomáš ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
This thesis describes design and development of an aplication for reidentification of vehicles in multiple camera streams. It explains methods of image processing, object detection and identification of symbols. Created application uses OpenCV library. It is written in the C++ language, standart C++14. Furthemore, this thesis describes testing of application on input data including training of the reference data.
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System for Automatic Parking Access Based on License Plate Recognition
Václavek, Patrik ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
Goal of this thesis was to design and implement system operating in real time, which manages to detect incoming vehicle to the car park terminal, recognize its licence plate and automatically decide on its admission. System uses the Gaussian Mixture Model algorithm for detection of incoming vehicle. For reliable localization of licence plate are used two methods, the first one uses of extraction of Maximally Stable Extremal Regions (MSERs), the second one uses of Top-Hat transformation. Support Vector Machine (SVM) algorithm is used to decide, whether is the found area a licence plate. Character classification is performed using artificial neural network. For implementation was used library OpenCV. Thanks to optimalization is the extraction of MSERs accelerated up to seven times. The accomplished success rate in case of licence plate localization is 92,47% and in case of classification of characters is 90,03%.
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Vehicle Collision Detection
Kruták, Martin ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This bachelor thesis decribes a system for detection and tracking of multiple vehicles from a surveillance camera with a collision detection. The focus is on detection and prediction of collisions of vehicles in one direction - towards the camera. System is not fully automatic, meaning that some initial settings are needed (e.g. lines on the road) to quarantee a good functionality of the system. Accurate vehicles' contour is obtained in the detection phase, and object centroids are calculated. Each detected vehicle is assigned to the specific lane and tracked separately. This thesis then describes the method of prediction and detection of a collision. A rectangle is created around the ground part of every vehicle. This rectangle of each of the vehicles is enlarged and checked for the overlaps. Those rectangles that overlaps are then subject to further analysis for the collision detection. Experimental results show a success rate of 72 % for the accurate rectangle construction being a crucial part for the collision detection. The advantage of the proposed system is its possible usage in surveillance cameras monitoring the traffic flow on highways.
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Section Speed Measurement for Traffic Analysis
Kubíčková, Pavla ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
This bachelor thesis focuses on section speed measurement for traffic analysis. This thesis desribes existing methods of detection of license plates and classification of their characters. Methods of cascade classifier and classifier SVM are described in this work. Evaluation of individual parts of the system is processed in the final section.
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Context-Aware Notification Filter for Android
Jaklovský, Samuel ; Špaňhel, Jakub (referee) ; Szentandrási, István (advisor)
The goal of this thesis is to develop an application for devices running Android which will determine user profile, based on obtained context, and apply user pre-defined sound settings for this profile. The thesis contains a description of common theory and design of user interface which was implemented as fully operational application. The application uses Naive Bayes classifier and Decision tree for determining the user profile. The functionality of the application was successfully tested by twenty users. The average ratings in the questionnaires were about eight and a half points from a possible maximum of ten. These results can be considered successful.
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Detection of Vehicles in Image
Špaňhel, Jakub ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This thesis aims to implement the vehicle detection and tracking method based on the motion model suitable for real-time processing. The first part includes analysis of the commonly used methods. The second part introduces principles of implemented method. This method consists of low-level features extraction, the spatiotemporal profiling of extracted features and image intensities, and classification of obtained traces based on HMM. Subsequently experiments using this trustworthy method are conducted to locate areas of potential method improvements.
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