Abstract
The future of traffic calls for intelligent transport systems, which can handle the increased demands and expectations of future traffic. This thesis examines the possibilities for creating a highway monitoring system. Highway monitoring is important in the future because traffic will increase. The purpose of this thesis is to build and evaluate such a system with focus on high performance and accuracy while respecting the privacy of drivers on the highway. It presents the design of the system based on different image processing and machine learning methods, with evaluations of their individual performance and accuracy, as well as the overall performance of the system. The results show that it is possible to build a highway monitoring system on relative mundane hardware, while still attaining a specific accuracy above 90% in most cases and a generalized performance of above 100 FPS.
This thesis contributes to the ongoing research in intelligent transport systems by proposing a coherent highway monitoring system with great performance and strong privacy principles. It shows that privacy does not have to impact the functionality or performance of a highway monitoring system. It serves as an indication that privacy is a topic that is worth researching in combination with different systems. This thesis also discusses the implications of the proposed system for further investigation in validating and improving performance and in how different types of data collectors can work together to form a coherent highway monitoring system.