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Detection and Tracking of Small Moving Objects
Záděra, David ; Mlích, Jozef (referee) ; Juránek, Roman (advisor)
This thesis summarizes and describes the methods for detecting and tracking moving objects in video. The detection methods discussed in the working are background subtraction, segmentation and detection methods using classifiers. From tracking methods are presented Kalman filter, TLD method and particle filter, which is described in detail in a separate chapter. Particle filter is a tracking method that calculates a new estimate of the state of the object based on a set of particles. These particles are defined by their positions and weights. For each next state are these values iteratively updated by measurment and resampling. From the acquired knowledge and expertise has been created a program that demonstrates the activity of the particle filter. This program is part of the DVD, where are also the samples and test results.
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Robot Localization Using OpenStreet Map
Rajnoch, Zdeněk ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
Goal of this thesis is localization of mobile robot in OpenStreet map segment. Robot IMU, odometry and compass sensors are used for trajectory reconstruction, which is compared to reference GPS trajectory. Extended Monte Carlo localization and clusterization are used for robot localization. Software is implemented in C++ with ROS middleware.
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Personal Navigation Based on Wireless Networks and Inertial Sensors
Kaňa, Zdeněk ; Raida, Zbyněk (referee) ; Soták,, Miloš (referee) ; Bradáč, Zdeněk (advisor)
Tato práce se zaměřuje na vývoj navigačního algoritmu pro systémy vhodné k lokalizaci osob v budovách a městských prostorech. Vzhledem k požadovaným nízkým nákladům na výsledný navigační systém byla uvažována integrace levných inerciálních senzorů a určování vzdálenosti na základě měření v bezdrátových sítích. Dále bylo předpokládáno, že bezdrátová síť bude určena k jiným účelům (např: měření a regulace), než lokalizace, proto bylo použito měření síly bezdrátového signálu. Kvůli snížení značné nepřesnosti této metody, byla navrhnuta technika mapování ztrát v bezdrátovém kanálu. Nejprve jsou shrnuty různé modely senzorů a prostředí a ty nejvhodnější jsou poté vybrány. Jejich efektivní a nové využití v navigační úloze a vhodná fůze všech dostupných informací jsou hlavní cíle této práce.
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Simulation of Robotic Search of Lost Radiation Sources
Cihlář, Miloš ; Lázna, Tomáš (referee) ; Žalud, Luděk (advisor)
Simulátory, společnostmi zabývající se robotikou hodně využívané, hrají důležitou roli při výzkumu robotů. Zrychlují, zjednodušují, zlevňují a usnadňují vývoj softwaru a algoritmů. Magisterská práce se proto zabývá návrhem systému, založeného na ROS2 a Gazebo simulátoru, umožňující simulaci pozemních robotů ve vnějším prostředí s možností hledat ztracené radiační zdroje. Práce navrhuje několik metod vytváření prostředí v Gazebo simulátoru včetně návrhu prostředí z mračna bodů a je vytvořen model čtyřkolového, smykově řízeného mobilního pozemního robota. Chování robota v simulátoru bylo ověřeno a upraveno pomocí teoretického dynamického popisu robota. Před simulací algoritmů pro hledání ztracených radiačních zdrojů je navržena metoda sledování referenční trajektorie pomocí proporcionálně integračního (PI) a lineárně kvadratického (LQ) regulátoru a navrhuje metodu k simulaci zdroje radiace a jeho měření. Hledání radiačního zdroje jsou použity dvě typově odlišné metody, kdy jedna je založena na prozkoumání celé oblasti a vytváří mapu radiace, a druhá metoda založená na částicovém filtru aktivně hledá ztracený zdroj záření.
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Traffic Monitoring from Aerial Video Data
Babinec, Adam ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis proposes a system for extraction of vehicle trajectories from aerial video data for traffic analysis. The system is designed to analyse video sequence of a single traffic scene captured by an action camera mounted on an arbitrary UAV flying at the altitudes of approximately 150 m. Each video frame is geo-registered using visual correspondence of extracted ORB features. For the detection of vehicles, MB-LBP classifier cascade is deployed, with additional step of pre-filtering of detection candidates based on movement and scene context. Multi-object tracking is achieved by Bayesian bootstrap filter with an aid of the detection algorithm. The performance of the system was evaluated on three extensively annotated datasets. The results show that on the average, 92% of all extracted trajectories are corresponding to the reality. The system is already being used in the research to aid the process of design and analysis of road infrastructures.
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Moving Objects Detection in Video Sequences
Němec, Jiří ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This thesis deals with methods for the detection of people and tracking objects in video sequences. An application for detection and tracking of players in video recordings of sport activities, e.g. hockey or basketball matches, is proposed and implemented. The designed application uses the combination of histograms of oriented gradients and classification based on SVM (Support Vector Machines) for detecting players in the picture. Moreover, a particle filter is used for tracking detected players. The whole system was fully tested and the results are shown in the graphs and tables with verbal descriptions.
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Parametrization of Image Point Neighborhood
Zamazal, Zdeněk ; Bařina, David (referee) ; Zemčík, Pavel (advisor)
This master thesis is focused on parametrization of image point neighborhood. Some methods for point localization and point descriptors are described and summarized. Gabor filter is described in detail. The practical part of thesis is chiefly concerned with particle filter tracking system. The weight of each particle is determined by the Gabor filter.
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Navigation of mobile robots
Rozman, Jaroslav ; Matoušek,, Václav (referee) ; Šolc, František (referee) ; Zbořil, František (advisor)
Mobile robotics has been very discussed and wide spread topic recently. This due to the development in the computer technology that allows us to create better and more sophisticated robots. The goal of this effort is to create robots that will be able to autonomously move in the chosen environment. To achieve this goal, it is necessary for the robot to create the map of its environment, where the motion planning will occur. Nowadays, the probabilistic algorithms based on the SLAM algorithm are considered standard in the mapping in these times. This Phd. thesis deals with the proposal of the motion planning of the robot with stereocamera placed on the pan-and-tilt unit. The motion planning is designed with regard to the use of algorithms, which will look for the significant features in the pair of the images. With the use of the triangulation the map, or a model will be created. The benefits of this work can be divided into three parts. In the first one the way of marking the free area, where the robot will plan its motion, is described. The second part describes the motion planning of the robot in this free area. It takes into account the properties of the SLAM algorithm and it tries to plan the exploration in order to create the most precise map. The motion of the pan-and-tilt unit is described in the third part. It takes advantage of the fact that the robot can observe places that are in the different directions than the robot moves. This allows us to observe much bigger space without losing the information about the precision of the movements.
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Optimal methods for sparse data exchange in sensor networks
Valová, Alena ; Poměnková, Jitka (referee) ; Rajmic, Pavel (advisor)
This thesis is focused on object tracking by a decentralized sensor network using fusion center-based and consensus-based distributed particle filters. The model includes clutter as well as missed detections of the object. The approach uses sparsity of global likelihood function, which, by means of appropriate sparse approximation and the suitable dictionaty selection can significantly reduce communication requirements in the decentralized sensor network. The master's thesis contains a design of exchange methods of sparse data in the sensor network and a comparison of the proposed methods in terms of accuracy and energy requirements.
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Compressive sampling for effective target tracking in a sensor network
Klimeš, Ondřej ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The master's thesis deals with target tracking. For this a decentralized sensor network using distributed particle filter with likelihood consensus is used. This consensus is based on a sparse representation of local likelihood function in a suitable chosen dictionary. In this thesis two dictionaries are compared: the widely used Fourier dictionary and our proposed B-splines. At the same time, thanks to the sparsity of distributed data, it is possible to implement compressed sensing method. The results are compared in terms of tracking error and communication costs. The thesis also contains scripts and functions in MATLAB.
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