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Vehicle License Plate Detection and Recognition Software
Masaryk, Adam ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The aim of this bachelor thesis is to design and develop software that can detect and recognize license plates from images. The software is divided into 3 parts - license plates detection, detector output processing and license plates characters recognition. We decided to implement detection and recognition using modern methods using convolutional neural networks.
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Detection of Graffiti Tags in Image
Pavlica, Jan ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti tags in the image. Graffiti tagsare the most common expression of graffiti, which serves as the author’s signature. In the thesis, state-of-the-art detection systems were tested; the most effective one is the Single Shot MultiBox Detector. The result has reached 75.7% AP.
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Information Portal for Students
Krejčí, Petr ; Špaňhel, Jakub (referee) ; Dytrych, Jaroslav (advisor)
This work contains students' communication analysis, the design of new information system for students and its implementation. New system integrates the social network Facebook, the Google Docs and the Google Calendar. The result of this master's thesis is working system written in Java language. The system is based on the students' communication analysis and the requirements of the Students' Union.
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Counting Crates in Images
Mičulek, Petr ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
V této práci se zabývám tématem počítání beden v obrazových datech pomocí technik hlubokého učení. V práci jsem navrhl řešení pro počítání beden, které představuji na fotkách krabiček sirek. Ačkoli původní řešení počítalo s využitím datové sady beden ze skladu pivovaru, sada nakonec nebyla dodána a na doporučení vedoucího práce byly pro řešení vybrány bloky krabiček sirek. Implementované řešení využívá plně konvoluční neuronovou síť založenou na klasifikaci, umožňující výstup ve vysokém rozlišení. Tato síť je trénována na výřezech fotek z datové sady, díky čemuž je řešení rychlé a síť je vhodná i pro použití na menších datových sadách. Síť detekuje ve fotkách klíčové body krabiček sirek, které jsou následně zpracovány algoritmem pro odhad klíčových bodů z predikce sítě a výpočet finálního počtu beden. Na validačním datasetu dosahuje řešení následujících výsledků: ve 12,5 % případů predikce selže a ve zbylých případech má průměrnou absolutní odchylku (MAE) 11,14. Pomocí rozsáhlých experimentů bylo řešení vyhodnoceno a výsledky potvrzují, že tento přístup může být použit pro počítání objektů.
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Detection of Vehicles in Image and Video
Petráš, Adam ; Zemčík, Pavel (referee) ; Špaňhel, Jakub (advisor)
This bachelor thesis is focused on vehicle detection. The thesis deals with the method of vehicle detection using convolutional neural networks, their structures and models. All scripts were implemented using python programming language with Tensorflow Object Detection API interface. The first part of this thesis was devote to the structures of popular neural networks and models of detection neural networks. The next chapter deals with the most famous frameworks that are used for machine learning. Three neural network models were selected and trained on the COD20K dataset. The result of this thesis is statistics that discuss the efficiency and performance of each model on trained dataset and compare performance without displaying video on Nvidia RTX 2060, where the performace archieved by SSD MobileNet V2 network was 300FPS and Nvidia Tegra TX2 8GB, whose performace reached almost 44FPS.
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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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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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Timetable Planning Software
Čillo, Vladimír ; Špaňhel, Jakub (referee) ; Dytrych, Jaroslav (advisor)
This work deals with timetabling problems at the Faculty of Information Technology of Brno University of Technology. The aim of this thesis is to design and implement new application to support manual timetable planning, that will offer some innovations in comparison with current state. Implemented application is based on client-server architecture, at which client and server communicate by means of REST interface. Application offers functions for preprocessing of input data, as well as functions for analysis of created timetables. Data can be exported in HTML format.
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Mobile App for Collecting Detailed Statistics from Basketball Matches
Grenar, Petr ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
The aim of this thesis is to develop a mobile application for collecting basketball statistics. Within the application I focus on collecting non-traditional statistics (for example counterattacks, shooting techniques) that serve coaches as a feedback to their training plan. Of course the application also allows collect classic basketball statistics such as shooting and fouls. All data are stored in the cloud database which is based on Google Firebase technology that allows real-time synchronization across devices. Appearance is designed with Material Design which recommends how the application should look like. The controls are designed to allow users to enter data as quickly as possible during a match.
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