Parking Space Monitoring and ID Based Car Tracking
Name
Rasmus Vahtra
Abstract
Parking space monitoring research has been going on for some time already, but the field is still being widely researched. As neural networks are becoming better, they can be utilised in more areas, including parking space monitoring. This thesis takes an approach with YOLO that is capable of performing detections in real-time. A short summary of YOLO's previous versions and its advantages over other neural networks is given. In addition, this thesis also takes a look at unique ID based detected object tracking by using Kalman filter. The workflow of Kalman filter is explained. The overall design of the software used in the thesis is explained and a method for parking space monitoring that, to the author's knowledge, has not been documented before, is offered. Results of the thesis are analysed and solutions are provided for some of the encountered issues. Some of the future possible improvements are explained.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Prof. Gholamreza Anbarjafari, Egils Avots, MSc
Defence year
2019