|
|
Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
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.
|
|
|
Acceleration of Face Recognition Algorithm with Neural Compute Stick 2
Mičánková, Eva ; Beran, Jan (referee) ; Goldmann, Tomáš (advisor)
This thesis focuses on the issue of facial recognition in a face image using neural networks and its acceleration. It provides an overview of previously used techniques and addresses the use of currently dominant convolutional neural networks to solve this issue. The work also focuses on acceleration mechanisms that can be used in this area. Based on the knowledge of the issue, a system based on the concept of edge computing was created, which can be used as a home security system connected to an IP camera, which sends a notification about the presence of an unknown person in a guarded area.
|
|
| |
|
|
Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
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.
|
guest ::
