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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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Automatic Traffic Video Surveillance: Fine-Grained Recognition of Vehicles and Automatic Speed Measurement
Sochor, Jakub ; Elder, James (referee) ; Svoboda,, Tomáš (referee) ; Herout, Adam (advisor)
V rámci této dizertační práce se zaměřuji na Inteligentní dopravní systémy a Počítačové vidění - především automatické měření rychlosti a rozpoznání automobilů podle typů. Rozpoznání automobilů podle typů je úkol, ve kterém system má predikovat přesný typ (např. Škoda Octavia combi mk2) pro daný obrázek automobilu. Publikoval jsem dva články, které popisují navržený přístup k tomuto problému a tvoří jádro této dizertace. Prezentovaná metoda je založena na 3D obalových kvádrech postavených okolo automobilů, které jsou následně využity pro rozbalení obrázku automobilu do roviny a tudíž normalizaci vstupu neuronové sítě, která dělá následné rozpoznání. Přístup byl dále rozpracován v druhé publikaci, kde je navržena metoda pro určení tohoto 3D obalového kvádru z jediného obrázku - tudíž není nutné mít zkalibrovanou kameru. Experimentální výsledky ukazují, že navržená metoda zlepšuje úspěšnost rozpoznání o 12 procentních bodů - chyba rozpoznání je redukována o 50 procent.Při měření rychlosti má systém za úkol odhadnout rychlost projíždějících aut z videa. Cílem je také, ať měření probíhá plně automaticky bez jakékoli manuální kalibrace. Jelikož neexistoval žádný dataset, který by obsahoval velké množství průjezdů s přesně změřenou rychlostí, tak jsme nejprve takovýto dataset pořídili. Dále jsem navrhnul metodu pro plně automatickou kalibraci dopravní dohledové kamery což umožňuje měřit rychlost automobilů pozorovaných touto kamerou. Metoda je založena na odhadu kalibrace pomocí detekovaných úběžníků scény a následného zarovnání 3D modelů několika běžných typů automobilů. Experimentální výsledky ukazují, že navržená metoda dosahuje průměrné chyby měření rychlosti 1,10 km/h.
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Fine-Grained Vehicle Recognition from Traffic Surveillance Camera
Mencner, Pavel ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
The aim of this thesis is image based detection of vehicles from traffic surveillance camera and fine-grained vehicle type recognition (manufacturer and model). In the thesis the Unpack normalization method is implemented which transforms the vehicle image into its apparent flat representation in order to increase the classifier's success rate. The Unpack method make use of 3D bounding box of the vehicle. This bounding box is constructed during test period using the information of vehicle contour and direction toward vanishing points. The thesis involve accuracy comparison between direct and Unpack classification methods. The proposed solution is based on several related parts that benefit from convolutional neural networks. These parts are: vehicle detection from image data, estimation of the directions towards vanishing points solved as classification task, vehicle contour detection using convolutional Encoder-Decoder network and fine-grained vehicle type classification. Using Unpack based classification the 2% accuracy improvement against direct classification has been achieved, resulting in 86% overall success rate. The outcome of this thesis is fine-grained vehicle classification system that works with traffic surveillance video without any viewpoint limitations.
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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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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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Fast Re-Calibration of PTZ Camera for Traffic Analysis
Dřevo, Aleš ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis deals with problematics of PTZ-camera re-calibration during movement. The objective of this work is to keep the camera in calibration mode from default status when the known positions of Vanishing Points are in the image. With their use during movement, which is changing with motion of the camera, their positions are kept with help of two implemented methods. The first method is based on the principle of homography, the second on the principle of cross ratio. The results show that both of these methods work especially for keeping the positions of First Vanishing Points. In the case of the Second Vanishing Points there appear various problems and the results are often quite inaccurate.
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Cloud Application for Traffic Analysis
Valchář, Vít ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
The aim of this thesis is to create a cloud application for traffic analysis without knowing anything about the system. The only input is address of the web camera pointing at traffic. This application is build on existing solution which is further enhanced. New modules for removing obstacles (such as lamppost covering part of the road) and splitting overlapping cars were added. The whole cloud solution consists of multiple components which communicates by HTTP messages and are controlled by web interface.
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Parallel Deep Learning
Šlampa, Ondřej ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
Aim of this thesis is to propose how to evaluate favourableness of parallel deep learning. In this thesis I analyze parallel deep learning and I focus on its length. I take into account gradient computation length and weight transportation length. Result of this thesis is proposal of equations, which can estimate the speedup on multiple workers. These equations can be used to determine ideal number of workers for training.
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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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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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