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Recognition of Driving Lane Borders in Video from On Board Camera
Letovanec, Lukáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This thesis is dedicated to the issue of driving lane borders recognition in frames of an onboard camera. In this thesis, an architecture of a deep convolutional neural network is introduced, by means of which the said problem is dealt with. The net was trained on a large dataset using gradient descent algorithm. The trained model has demonstrated the ability to recognize borders of a driving lane well in different situations and conditions. The result of the thesis confirms that deep convolutional neural networks are a suitable tool for driving lane borders recognition.
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Deep Learning for Facial Recognition in Video
Mihalčin, Tomáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This diploma thesis focuses on a face recognition from a video, specifically how to aggregate feature vectors into a single discriminatory vector also called a template. It examines the issue of the extremely angled faces with respect to the accuracy of the verification. Also compares the relationship between templates made from vectors extracted from video frames and vectors from photos. Suggested hypothesis is tested by two deep convolutional neural networks, namely the well-known VGG-16 network model and a model called Fingera provided by company Innovatrics. Several experiments were carried out in the course of the work and the results of which confirm the success of proposed technique. As an accuracy metric was chosen the ROC curve. For work with neural networks was used framework Caffe.
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Recognition of Driving Lane Borders in Video from On Board Camera
Letovanec, Lukáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This thesis is dedicated to the issue of driving lane borders recognition in frames of an onboard camera. In this thesis, an architecture of a deep convolutional neural network is introduced, by means of which the said problem is dealt with. The net was trained on a large dataset using gradient descent algorithm. The trained model has demonstrated the ability to recognize borders of a driving lane well in different situations and conditions. The result of the thesis confirms that deep convolutional neural networks are a suitable tool for driving lane borders recognition.
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Deep Learning for Facial Recognition in Video
Mihalčin, Tomáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This diploma thesis focuses on a face recognition from a video, specifically how to aggregate feature vectors into a single discriminatory vector also called a template. It examines the issue of the extremely angled faces with respect to the accuracy of the verification. Also compares the relationship between templates made from vectors extracted from video frames and vectors from photos. Suggested hypothesis is tested by two deep convolutional neural networks, namely the well-known VGG-16 network model and a model called Fingera provided by company Innovatrics. Several experiments were carried out in the course of the work and the results of which confirm the success of proposed technique. As an accuracy metric was chosen the ROC curve. For work with neural networks was used framework Caffe.
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