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Hand gesticulation recognition in image
Mráz, Stanislav ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
This master’s thesis is dealing with recognition of an easy static gestures in order to computer controlling. First part of this work is attended to the theoretical review of methods used to hand segmentation from the image. Next methods for hang gesture classification are described. The second part of this work is devoted to choice of suitable method for hand segmentation based on skin color and movement. Methods for hand gesture classification are described in next part. Last part of this work is devoted to description of proposed system.
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Running Motion Analysis
Eliáš, Radoslav ; Kolářová, Jana (referee) ; Goldmann, Tomáš (advisor)
Cieľom tejto práce je analyzovať pohyb a držanie tela pri behu. Systém pracuje so záznamom z dvoch kamier, zboku a zozadu. Využíva nástroj na detekciu postoja ľudského tela založenú na konvolučnej metóde. Práca porovnáva niekoľko detektorov. Výsledný systém používa detektor OpenPose a implementuje knižnicu s výpočtami pre rôzne metriky používane na ohodnotenie formy behu. Výsledky sú zobrazené v multiplatformnej aplikácii. Ohodnotená bola niekoľkými experimentmi na osobnej dátovej sade videí behu.
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RoboAuto - Detection of Moving Objects
Štibinger, Petr ; Žák, Pavel (referee) ; Juránek, Roman (advisor)
In this work is possible to find out something about detection of moving objects from moving camera. Method that was used is MTI (moving target indication), which is used for detection from air and can be applied on ground vehicles. Introduced will be work with SURF (for detection of interesting points in image) and histograms (for detection moving objects).
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Processing Sensor Data from a Wearable Device by Machine Learning
Hlavačka, Martin ; Dobeš, Petr (referee) ; Herout, Adam (advisor)
The goal of this master's thesis is to analyze the situation of wearable devices with the Android Wear operating system and recognition capabilities of various movement activities using neural networks. The primary focus is therefore on identifying and describing the most appropriate tool for recognizing dynamic movements using machine learning methods based on data obtained from this type of devices. The practical part of the thesis then comments on the implementation of a stand-alone Android Wear application capable of recording and formatting data from sensors, training the neural network in a designed external desktop tool, and then reusing trained neural network for motion recognition directly on the device.
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People movement analysis in area of interest
Kratochvíl, Jakub ; Bastl, Petr (referee) ; Valach, Soběslav (advisor)
This bachelor thesis deals with the possibilities of monitoring the movement of people in areas of interest. The theoretical part presents various options for motion detection in the stores, from the simplest PIR detectors to the use of thermal cameras or wireless Wi-Fi interfaces. The proposed solution then deals with the detection of the movement of people by using computer vision.
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Exploitation of Machine Learning for Identification of Feeder Rod Movement
Vele, Patrik ; Vašíček, Zdeněk (referee) ; Šimek, Václav (advisor)
The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.
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Exploitation of Machine Learning for Identification of Feeder Rod Movement
Vele, Patrik ; Vašíček, Zdeněk (referee) ; Šimek, Václav (advisor)
The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.
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People movement analysis in area of interest
Kratochvíl, Jakub ; Bastl, Petr (referee) ; Valach, Soběslav (advisor)
This bachelor thesis deals with the possibilities of monitoring the movement of people in areas of interest. The theoretical part presents various options for motion detection in the stores, from the simplest PIR detectors to the use of thermal cameras or wireless Wi-Fi interfaces. The proposed solution then deals with the detection of the movement of people by using computer vision.
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Running Motion Analysis
Eliáš, Radoslav ; Kolářová, Jana (referee) ; Goldmann, Tomáš (advisor)
Cieľom tejto práce je analyzovať pohyb a držanie tela pri behu. Systém pracuje so záznamom z dvoch kamier, zboku a zozadu. Využíva nástroj na detekciu postoja ľudského tela založenú na konvolučnej metóde. Práca porovnáva niekoľko detektorov. Výsledný systém používa detektor OpenPose a implementuje knižnicu s výpočtami pre rôzne metriky používane na ohodnotenie formy behu. Výsledky sú zobrazené v multiplatformnej aplikácii. Ohodnotená bola niekoľkými experimentmi na osobnej dátovej sade videí behu.
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Processing Sensor Data from a Wearable Device by Machine Learning
Hlavačka, Martin ; Dobeš, Petr (referee) ; Herout, Adam (advisor)
The goal of this master's thesis is to analyze the situation of wearable devices with the Android Wear operating system and recognition capabilities of various movement activities using neural networks. The primary focus is therefore on identifying and describing the most appropriate tool for recognizing dynamic movements using machine learning methods based on data obtained from this type of devices. The practical part of the thesis then comments on the implementation of a stand-alone Android Wear application capable of recording and formatting data from sensors, training the neural network in a designed external desktop tool, and then reusing trained neural network for motion recognition directly on the device.
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