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Traffic surveillance system
Vaňo, Jakub ; Materna, Zdeněk (referee) ; Juránek, Roman (advisor)
Táto práca sa zaoberá rôznymi metódami pre systémy sledovania dopravy a zberom dát s tým spojeným. Boli naštudované rôzne metódy detekcie a sledovania objektov so zameraním na neurónové siete, spomedzi ktorých bol vybraný a použitý model YOLO na implementáciu detekcie vozidiel. Konečná implementácia systému sledovania dopravy obsahovala správcu úloh a tri rôzne programy na doplnenie dát. Komunikácia systému bola dosiahnutá pomocou MQTT agenta. Výsledky krátkodobého testovania systému potvrdzujú jeho schopnosť zvládnuť zber a spracovanie dát v reálnom čase z viacerých kamier. Výsledky zozbieraných dát boli analyzované pomocou Grafany. Po dostatočnom čase zberu informácií by tieto údaje mohli byť potenciálne použité na zlepšenie dopravy.
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Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
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Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
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