National Repository of Grey Literature 41 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
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.
Synthetic Dataset Generator for Traffic Analysis
Svoreň, Ondrej ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This bachelor thesis deals with the creation and customization of synthetic dataset genera tor for traffic analysis. It focuses on traffic analysis by means of computer vision, methods and conditions of creating the generator of synthetic dataset, possible application of achie ved results in machine learning and additional development opportunities. Using available automobile photographs from the Czech Republic, Slovakia, Poland and Hungary, a synthe tic license plate number generator was created, which, after graphical adjustment and after joining with the vehicle photographs creates the resulting dataset for machine learning. The solution itself is divided into the three scripts written in Python using the OpenCV library. The resulting dataset serves as an input for the machine learning system to re-identify the license plate numbers from photographs captured in the flow of traffic.
Deep Learning for Facial Recognition in Video
Jeřábek, Vladimír ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with face recognition in video using neural networks. In the beginning, there is described the process of selection and verification of convolution neural network to generate feature vectors from images of different identities. In the next part, this work deals with the aggregation of feature vectors from video frames. Aggregation takes place through aggregation neural networks. At the end of this work, the results obtained by the aggregation methods are discussed.
Mobile Application for Studis BUT
Smyčka, Jan ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
Bachelor thesis deals with the development of mobile application in Android for VUT information   system. The theoretical part of the thesis describes Android operating system and its basic elements   used in the application development process, the text also describes the architectural design of   mobile applications. The main part of the thesis deals with the implementation of the application and   its testing on the users. In the final part of the thesis, the application development and user testing   are assessed, and further possible development of the application for the future is proposed.
Speed Measurement of Vehicles from Surveillance Camera
Jaklovský, Samuel ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This master's thesis is focused on fully automatic calibration of traffic surveillance camera, which is used for speed measurement of passing vehicles. Thesis contains and describes theoretical information and algorithms related to this issue. Based on this information and algorithms, a comprehensive system design for automatic calibration and speed measurement was built. The proposed system has been successfully implemented. The implemented system is optimized to process the smallest portion of the video input for the automatic calibration of the camera. Calibration parameters are obtained after processing only two and half minutes of input video. The accuracy of the implemented system was evaluated on the dataset BrnoCompSpeed. The speed measurement error using the automatic calibration system is 8.15 km/h. The error is mainly caused by inaccurate scale acquisition, and when it is replaced by manually obtained scale, the error is reduced to 2.45 km/h. The speed measuring system itself has an error of only 1.62 km/h (evaluated using manual calibration parameters).
Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings.  In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
Deep Learning for Facial Recognition in Video
Mihalčin, Tomáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This diploma thesis focuses on a face recognition from a video, specifically how to aggregate feature vectors into a single discriminatory vector also called a template.   It examines the issue of the extremely angled faces with respect to the accuracy of the verification. Also compares the relationship between templates made from vectors extracted from video frames and vectors from photos. Suggested hypothesis is tested by two deep convolutional neural networks, namely the well-known VGG-16 network model and a model called Fingera provided by company Innovatrics. Several experiments were carried out in the course of the work and the results of which confirm the success of proposed technique. As an accuracy metric was chosen the ROC curve. For work with neural networks was used framework Caffe.
Augmented Reality for Desk Board Game
Richter, Jiří ; Sochor, Jakub (referee) ; Beran, Vítězslav (advisor)
Cílem této práce je vytvořit systém, který bude vylepšovat zážitek z hraní deskových a především karetních her, promítáním relevantních multimediálních informací do herního prostoru. V první části jsou popsány přínosy deskových a karetních her. Dále jsou představeny principy rozšířené reality a jejich použití při návrhu takového systému. Navržený systém je rozdělen na tři moduly. Prvním z nich je modul, který analyzuje stav hry na stole v reálném světe --- poskytuje informace o aktivitě hráčů a jaké typy herních objektů se vyskytují na stole. Druhým je modul, který se stará o řízení celého systému, poskytuje uživatelské rozhraní pro kalibraci systému a implementuje stavový prostor hry, pro kterou je systém určen. Třetím je modul, který poskytuje výstup systému --- vytváří multimediální obsah, který je po-té promítnut na stůl, a je relevantní k aktuálnímu stavu hry. Na závěr je navržený systém implementován pro karetní hru Bang, otestována jeho schopnost udržovat krok se stavem hry v reálném světě a provedeno testování na uživatelích.
Localisation of Mobile Robot in the Environment
Urban, Daniel ; Sochor, Jakub (referee) ; Veľas, Martin (advisor)
This diploma thesis deals with the problem of mobile robot localisation in the environment based on current 2D and 3D sensor data and previous records. Work is focused on detecting previously visited places by robot. The implemented system is suitable for loop detection, using the Gestalt 3D descriptors. The output of the system provides corresponding positions on which the robot was already located. The functionality of the system has been tested and evaluated on LiDAR data.
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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