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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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Odometry of Experimental Vehicle Car4
Štarha, Matěj ; Adámek, Roman (referee) ; Dobossy, Barnabás (advisor)
This thesis focuses on the creation of odometry for experimental vehicle Car4. First, necessary HW and SW improvements are carried out. A research of kinematic model and known algorithms for position estimation is then conducted. Two algorithms are implemented in Matlab and their functionality is verified using simulation followed by tests of the actual trajectory of the vehicle. The goal of this work is to create a basis for any following work on the vehicle, e.g. in autonomous driving.
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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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A Comparison Particle Filter for Searching a Radiation Source in Real and Simulated World
Cihlar, Milos ; Lazna, Tomas ; Zalud, Ludek
In this paper, we are focusing on comparing solutionsfor localizing an unknown radiation source in both aGazebo simulator and the real world. A proper simulation ofthe environment, sensors, and radiation source can significantlyreduce the development time of robotic algorithms. We proposeda simple sampling importance resampling (SIR) particle filter.To verify its effectiveness and similarities, we first tested thealgorithm’s performance in the real world and then in the Gazebosimulator. In experiment, we used a 2-inch NaI(Tl) radiationdetector and radiation source Cesium 137 with an activity of 330Mbq. We compared the algorithm process using the evolution ofinformation entropy, variance, and Kullback-Leibler divergence.The proposed metrics demonstrated the similarity between thesimulator and the real world, providing valuable insights toimprove and facilitate further development of radiation searchand mapping algorithms.
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Odometry of Experimental Vehicle Car4
Štarha, Matěj ; Adámek, Roman (referee) ; Dobossy, Barnabás (advisor)
This thesis focuses on the creation of odometry for experimental vehicle Car4. First, necessary HW and SW improvements are carried out. A research of kinematic model and known algorithms for position estimation is then conducted. Two algorithms are implemented in Matlab and their functionality is verified using simulation followed by tests of the actual trajectory of the vehicle. The goal of this work is to create a basis for any following work on the vehicle, e.g. in autonomous driving.
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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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Porovnání metod pro odhad omezených veličin s aplikací na ekonomická data
Musil, Karel ; Pavelková, Lenka (advisor) ; Hlávka, Zdeněk (referee)
The thesis introduces an overview of techniques for filtering of unobserved variables using a state-space representation of a model and state inequality constraints. It is mainly aimed at a derivation of the linear Kalman filter, its extension into a form of a non-linear filter and imposing state constraints. The state uniform model with noise bounds and the sequential importance sampling, as a method of particle filters using Monte Carlo simulations, are described as alternative methods. These three methods are applied on a simple semi-structural model for a monetary policy analysis. The filtration is based on Czech macroeconomic data and reflects an imposed non-negative state constraint on the interest rate. Results of the algorithms are compared and discussed.
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