Thesis Details
Vizuální systém pro detekci obsazenosti parkoviště pomocí hlubokých neuronových sítí
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 %.
Computer vision, car detection, deep neural networks, machine learning, GoogLeNet, background model, Robot Operating System
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Češka Milan, prof. RNDr., CSc. (DITS FIT BUT), člen
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT), člen
Vranić Valentino, doc. Ing., Ph.D. (FIIT STU), člen
@mastersthesis{FITMT19747,
author = "V\'{a}clav Str\'{a}nsk\'{y}",
type = "Master's thesis",
title = "Vizu\'{a}ln\'{i} syst\'{e}m pro detekci obsazenosti parkovi\v{s}t\v{e} pomoc\'{i} hlubok\'{y}ch neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2017,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/19747/"
}