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Visual Car-Detection on the Parking Lots Using Deep Neural Networks
Stránský, Václav ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
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Forensic analysis of handwriting for the Czech environment using artificial intelligence
Stejskal, Jan ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
The analysis of handwriting is an important area of research in modern science. However, it is a very complex process because handwritten text can take on various forms. The use of artificial intelligence for analyzing and identifying text from different authors is nothing new in the world. Research in this area is, however, slightly lagging behind in the Czech environment. For this reason, several convolutional network architectures were proposed and compared in this work in an effort to find the most suitable structure for solving this problem. Of all the trained and tested models, the model based on the ResNet18 architecture achieved the highest accuracy, with a success rate of 92.2 % on a self-made database of 1328 samples with a resolution of 750x256. This result suggests that with a sufficiently large and high-quality database, the problem can be solved even in the Czech environment with its more complicated character set.
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Visual Car-Detection on the Parking Lots Using Deep Neural Networks
Stránský, Václav ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
The concept of smart cities is inherently connected with efficient parking solutions based on the knowledge of individual parking space occupancy. The subject of this paper is the design and implementation of a robust system for analyzing parking space occupancy from a multi-camera system with the possibility of visual overlap between cameras. The system is designed and implemented in Robot Operating System (ROS) and its core consists of two separate classifiers. The more successful, however, a slower option is detection by a deep neural network. A quick interaction is provided by a less accurate classifier of movement with a background model. The system is capable of working in real time on a graphic card as well as on a processor. The success rate of the system on a testing data set from real operation exceeds 95 %.
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