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Deep Learning Algorithms on Embedded Devices
Hadzima, Jaroslav ; Boštík, Ondřej (referee) ; Horák, Karel (advisor)
Táto práca popisuje v súčastnosti široko používané architektúry a modely pre Hlboké Učenie, riešiace úlohu detekcie a klasifikácie objektov vo videu. Dôraz tu bude kladený na ich použiteľnosť na vstavaných zariadeniach. Postupne preberieme kroky a odvôvodňovanie pri výbere najlepšieho vstavaného systému pre našu aplikáciu. Ukážková aplikáci pozostáva hlavne z detekcie vozidiel a detekcie voľných parkovacích miest s využitím algoritmov Hlbokého Učenia. Táto aplikácia umožňuje monitorovať počet vozidiel, nachádzajúcich sa na parkovisku a zároveň rozhodnúť, či sa nachádzajú na prakovacom mieste alebo nie. Následne tu budú prebrané kroky nutné ku konfigurácii zariadenia s dôrazom na optimalizáciu hardvéru pre dosiahnutie čo najväčšej rýchlosti. V ďaľšej časti bude poskytnuté porovnanie vybraných modelov, ktoré budú porovnávané hlavne v kategóriách ako rýchlosť alebo F1 skóre. Najlepší kandidát bude použitý na riešenie našej aplikácie a následné testovanie jej vlastností s názvom Inteligentné parkovisko.
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Vehicle 3D Pose Estimation form Traffic Cameras
Pospíšil, Ondřej ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this bachelor thesis is to create a method for the 3D pose estimation of vehicles from traffic cameras. Existing methods for the car detection and the pose estimation of vehicles are described. Part of the thesis was to build a dataset for the purpose of training and experiments on the proposed car pose estimation method. Proposed method uses a convolutional neural network for regression of the car base in the image. Car pose is then projected into the road plane using homography. Experiments summarize training and the evaluation of the car pose estimation method and accuracy of manual vehicle annotation.
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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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Analysis of Parking Availability
Stránský, Václav ; Rozman, Jaroslav (referee) ; Marvan, Aleš (advisor)
This thesis deals with an analysis of parking availability. The task is to determine the occupancy of predefined parking places in the sequence of color images acquired by a fixed camera. The thesis describes the design, implementation, and testing of three utilized methods for car detection, i.e. background modelling, edge detection, and histogram comparison. These methods are combined in order to obtain a more robust and accurate solution. The proposed system has been successfully tested on several real scenarios.
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Detection of Vehicles in Image
Pomykal, Antonín ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This work deals with the possibility of detection of cars in the image using the characteristics of cars with custom created image features , which are made pursuant to Haar-like features, and using methods of AdaBoost to train and their detection. We introduce the possibilities and types of custom picture features, OpenCV library, which was used in the implementation of the program, and we show the results and the success of this combination of detection algorithms.
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Visual Car-Detection on the Parking Lots Using Deep Neural Networks
Stránský, Václav ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
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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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RoboAuto - Car Detection
Melo, Jakub ; Řezníček, Ivo (referee) ; Juránek, Roman (advisor)
This thesis deals with detection and tracking of cars viewed from behind. For the detection, Adaboost algorithm is used. The tracking is done using Kalman filter. In the first part of the work, theoretical background of the object detection and tracking is described. Experiments with classifier training and their results are presented in the second part of the work.
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Vehicle 3D Pose Estimation form Traffic Cameras
Pospíšil, Ondřej ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this bachelor thesis is to create a method for the 3D pose estimation of vehicles from traffic cameras. Existing methods for the car detection and the pose estimation of vehicles are described. Part of the thesis was to build a dataset for the purpose of training and experiments on the proposed car pose estimation method. Proposed method uses a convolutional neural network for regression of the car base in the image. Car pose is then projected into the road plane using homography. Experiments summarize training and the evaluation of the car pose estimation method and accuracy of manual vehicle annotation.
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