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Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (oponent) ; Goldmann, Tomáš (vedoucí práce)
The aim of this thesis is to make an application capable of recognizing people's faces based on a user-created database in drone footage. The database is made of pictures of people that should be identified in the footage. The output of this application is a video where the demanded people are labeled with their names. Some face detection and recognition state of the art solutions based on neural networks are compared in this work. The final solution consists of the MTCNN detector and a face embedding extractor based on ArcFace. The created multiplatform application allows to recognize people in drone footage even with face width of less than 20 pixels. The final solution was tested on a private dataset comprised of drone footage.
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Akcelerace algoritmu pro rozpoznávání obličeje pomocí Neural Compute Stick 2
Mičánková, Eva ; Beran, Jan (oponent) ; Goldmann, Tomáš (vedoucí práce)
Tato práce je zaměřena na problematiku rozpoznávání obličejů v obraze pomocí neuronových sítí a jejich akceleraci. Obsahuje souhrn dříve používaných technik a zabývá se využitím dnes dominujících konvolučních neuronovýh sítí pro řešení této problematiky. Práce se také zaměřuje na mechanismy akcelerace, které lze v této oblasti použít. Na základě znalostí problematiky získaných studiem byl vytvořen systém na konceptu edge computingu, který může být použit jako domácí bezpečnostní systém připojený k IP kameře, který zasílá upozornění o přítomnosti neznámé osoby ve střežené oblasti.
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Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (oponent) ; Goldmann, Tomáš (vedoucí práce)
The aim of this thesis is to make an application capable of recognizing people's faces based on a user-created database in drone footage. The database is made of pictures of people that should be identified in the footage. The output of this application is a video where the demanded people are labeled with their names. Some face detection and recognition state of the art solutions based on neural networks are compared in this work. The final solution consists of the MTCNN detector and a face embedding extractor based on ArcFace. The created multiplatform application allows to recognize people in drone footage even with face width of less than 20 pixels. The final solution was tested on a private dataset comprised of drone footage.
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