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Vision-based Algorithms for UAV Mimicking Control System Open Access


Other title
Control Systems
Vision-based Algorithms
Type of item
Degree grantor
University of Alberta
Author or creator
Martinez, Pablo
Supervisor and department
Barczyk, Martin (Mechanical Engineering)
Examining committee member and department
Ahmad, Rafiq (Mechanical Engineering)
Jagersand, Martin (Computer Science)
Barczyk, Martin (Mechanical Engineering)
Department of Mechanical Engineering

Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
Vision-based algorithms designed to detect and track UAVs from an onboard moving platform have been the focus of active and extensive research over the last decade, and dozens of algorithms have been tested, compared and optimized. However, the existing approaches tend to rely on specific features such as color or edges which may not be able to detect and track various types of flying quadcopters. This thesis implements a modified version of an existing vision-based algorithm, the Cascade Classifier, originally designed to recognize facial features and humans, and demonstrates its capability of detecting and track any type of quadcopter with great accuracy over a variety of backgrounds, in both indoors and outdoors flight conditions. The Cascade Classifier algorithm is demonstrated on two specific quadcopter models used for this study, the 3DR Solo and the Parrot AR.Drone 2.0. This thesis introduces a novel method to reduce the amount of information which needs to be processed by vision-based algorithms when tracking physical objects undergoing non-random motion in 3D space. This method employs a Kalman filter to predict the estimated position, velocity and acceleration of the tracked object in order to reduce the image area in which the tracked quadcopter is believed to be. This enables the Cascade Classifier algorithm, or any other type of vision-based detection algorithm to track the target vehicle while greatly reducing the required image processing time. Experimental testing proves that the proposed algorithm obtains good detection and tracking performance in real-time for both quadcopter types in indoor and outdoor flight scenarios, as well as the successful performance of the mimicking control system design.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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