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Masked face detection
Malý, Ondřej ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
The aim of this work is to study and test current methods for face detection on veiled faces and evaluate the results. In the first chapter, five selected methods are theoretically analyzed and in the second chapter the individual methods are evaluated, both for the Wider Face file and for the actual set of photos with veiled faces. Subsequently, the Dlib CNN method is improved for better detection of veiled faces and reprogrammed to detect the degree of veil from the tested image
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Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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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.
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Deep Learning for Object Detection
Paníček, Andrej ; Herout, Adam (referee) ; Teuer, Lukáš (advisor)
This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.
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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.
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Masked face detection
Malý, Ondřej ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
The aim of this work is to study and test current methods for face detection on veiled faces and evaluate the results. In the first chapter, five selected methods are theoretically analyzed and in the second chapter the individual methods are evaluated, both for the Wider Face file and for the actual set of photos with veiled faces. Subsequently, the Dlib CNN method is improved for better detection of veiled faces and reprogrammed to detect the degree of veil from the tested image
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|
|
Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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|
|
Deep Learning for Object Detection
Paníček, Andrej ; Herout, Adam (referee) ; Teuer, Lukáš (advisor)
This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.
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