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Tutoriál práce s OpenCV
Běhal, Lukáš ; Bařina, David (oponent) ; Beran, Vítězslav (vedoucí práce)
Tato bakalářská práce se zabývá identifikací a popisem základní činnosti doposud nezdokumentovaných metod v knihovně OpenCV. Konkrétně jsou zde popsány algoritmy FAST corner detector, Maximally stable extremal regions, LDetector, HOG people and object detector a One-way descriptor. Každá metoda obsahuje teoretickou část, deklaraci funkcí s popisem jejich parametrů a v neposlední řadě rovněž příklad použití. Pro každou z metod byla také vytvořena vzorová demo aplikace.
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An Autonomous Driver of a TORCS Racing Car
Běhal, Lukáš ; Kaštil, Jan (oponent) ; Jaroš, Jiří (vedoucí práce)
This work describes the TORCS simulator and optimization algorithms used in the field of autonomous driving competitions. The main purpose of this work is to design a new controller solution based on genetic algorithms. The controller's behavior can be divided into two main parts which are exploited during the distinct stages of the competition. The warm-up stage serves for the track model sampling and the race line optimization. The race stage logic then benefits from the data obtained in the warm-up stage. The track optimization is done by a Genetic algorithm while the track is divided into several segments optimized separately. A genetic algorithm is applied once again to the track trajectory to smooth out gaps caused by the segment composition. In this work was shown that the track sampling and race line optimization by a genetic algorithm can be done during the warm-up stage. This makes the controller suitable for an autonomous driver competitions.
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Tutoriál práce s OpenCV
Běhal, Lukáš ; Bařina, David (oponent) ; Beran, Vítězslav (vedoucí práce)
Tato bakalářská práce se zabývá identifikací a popisem základní činnosti doposud nezdokumentovaných metod v knihovně OpenCV. Konkrétně jsou zde popsány algoritmy FAST corner detector, Maximally stable extremal regions, LDetector, HOG people and object detector a One-way descriptor. Každá metoda obsahuje teoretickou část, deklaraci funkcí s popisem jejich parametrů a v neposlední řadě rovněž příklad použití. Pro každou z metod byla také vytvořena vzorová demo aplikace.
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|
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An Autonomous Driver of a TORCS Racing Car
Běhal, Lukáš ; Kaštil, Jan (oponent) ; Jaroš, Jiří (vedoucí práce)
This work describes the TORCS simulator and optimization algorithms used in the field of autonomous driving competitions. The main purpose of this work is to design a new controller solution based on genetic algorithms. The controller's behavior can be divided into two main parts which are exploited during the distinct stages of the competition. The warm-up stage serves for the track model sampling and the race line optimization. The race stage logic then benefits from the data obtained in the warm-up stage. The track optimization is done by a Genetic algorithm while the track is divided into several segments optimized separately. A genetic algorithm is applied once again to the track trajectory to smooth out gaps caused by the segment composition. In this work was shown that the track sampling and race line optimization by a genetic algorithm can be done during the warm-up stage. This makes the controller suitable for an autonomous driver competitions.
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