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Computer Vision Tracking of sUAS From a Pan/Tilt Platform

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dc.contributor.authorOgorzalek, Jeremy Patricken
dc.contributor.committeechairBlack, Jonathan T.en
dc.contributor.committeememberEngland, Scott L.en
dc.contributor.committeememberPsiaki, Mark L.en
dc.contributor.departmentAerospace and Ocean Engineeringen
dc.date.accessioned2019-06-25T08:01:13Zen
dc.date.available2019-06-25T08:01:13Zen
dc.date.issued2019-06-24en
dc.description.abstractThe ability to quickly, accurately, and autonomously identify and track objects in digital images in real-time has been an area of investigation for quite some time. Research in this area falls under the broader category of computer vision. Only in recent decades, with advances in computing power and commercial optical hardware, has this capability become a possibility. There are many different methods of identifying and tracking objects of interest, and best practices are still being developed, varying based on application. This thesis examines background subtraction methods as they apply to the tracking of small unmanned aerial systems (sUAS). A system combining commercial off-the-shelf (COTS) cameras and a pan-tilt unit (PTU), along with custom developed code, is developed for the purpose of continuously pointing at and tracking the motion of a sUAS in flight. Mixtures of Gaussians Background Modeling (MOGBM) is used to track the motion of the sUAS in frame and determine when to command the PTU. When the camera is moving, background subtraction methods are unusable, so additional methods are explored for filling this performance gap. The stereo vision capabilities of the system, enabled by the use of two cameras simultaneously, allow for estimation of the three-dimensional position and trajectory of the sUAS. This system can be used as a supplement or replacement to traditional tracking methods such as GPS and RADAR as part of a larger unmanned aerial systems traffic control (UTC) infrastructure.en
dc.description.abstractgeneralThe ability to quickly, accurately, and automatically identify and track targets in digital images has been of interest for some time now. Research in this area falls under the broader category of computer vision. Only in recent decades, with advances in computing power and commercial optical hardware, has this ability become a possibility. There are many different methods of identifying and tracking targets of interest, and best practices are still being developed, varying based on application. This thesis examines background subtraction methods as they apply to the tracking of small unmanned aerial systems (sUAS), commonly referred to as drones. A system combining cameras and a moving platform, along with custom developed code, is developed for the purpose of continuously pointing at and tracking the motion of an sUAS in flight. The system is able to map out the three-dimensional position of a flying sUAS over time.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:21173en
dc.identifier.urihttp://hdl.handle.net/10919/90578en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectsUASen
dc.subjectComputer Visionen
dc.subjectTarget Trackingen
dc.subjectPTUen
dc.titleComputer Vision Tracking of sUAS From a Pan/Tilt Platformen
dc.typeThesisen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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