Master's Thesis |

Highway Monitoring System

Mon, 14 Jan 2019 00:00:00 +0000

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.

Automated Lecturer-Tracking System

Mon, 10 Sep 2018 00:00:00 +0000

Abstract

Development of technology has brought some significant changes to the educational system, resulting in some new means of gathering the knowledge. In this project, we focus on the video lectures that provide significant benefits for all the students. We aim to enhance the way video lessons are recorded by introducing the system for automatic lecturer-tracking. This thesis introduces the new approach in implementing an automated lecturer-tracking system by using a smartphone as the replacement for a camera device and a processing unit. The proposed solution uses the YOLO real-time object detection system and tracking algorithms from iOS Vision framework to detect and track the lecturer. A motorized pan-tilt head rotates the smartphone based on the input the smartphone sends to it. Experimental results show that the system can perform the desired behavior of lecturer-tracking, eliminating the need for human help in the process of recording.

Computer Vision til EvoBotten

Mon, 13 Jun 2016 00:00:00 +0000

Abstract

Automated production using robots play a significant role in chemistry, biotechnology and microbiology. Robots that are designed to perform a specific task are in the long run cheaper and much more efficient than humans. In research, however, most tasks are done by humans even though many tasks are cumbersome and repetitive. What complicates the introduction of robots in research are the small variations in the task sequences that are frequently introduced. In this Master thesis we will contribute to a project called the EvoBot which seeks to make robots an integral part of research in order to lower costs and speed up the process of experimenting. We develop a proof-of-concept for a computer vision application for the EvoBot that enables it to find, locate and classify petri dishes and well plates. We present the design of the system implemented using the OpenCV framework along with physical modifications to the EvoBot. In addition to the vision system we develop a framework to test its precision and accuracy and lay ground work for future improvements. We evaluate different approaches based on accuracy and precision results of the detection methods that are experimented with. The evaluation indicates that detection without the use of tagging is feasible for use in the industry, with the introduction of some future improvements.