Fully Transparent Computer Vision Framework for Ship Detection and Tracking in Satellite Imagery
Thomas Wischgoll (Advisor); John Gallagher (Committee Member); Michael Raymer (Committee Member); Barry Milligan (Other)
Master of Science (MS)
Tracking of ships in satellite imagery is a challenging problem in remote sensing since it requires both object detection and object recognition. Most of the resources available only cover one of these problems and are often ﬁlled with machine learning techniques which are costly to train. Additionally, the techniques covered in these resources are often difﬁcult to replicate or may be hard to combine with other solutions to get a full tracking algorithm. The proposed framework offers a transparent and efﬁcient alternative to machine learning approaches and includes preprocessing, detection, and recognition needed for tracking. All components of the framework were created based on open source libraries to provide a transparent solution which can be easily modiﬁed for speciﬁc use cases.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.