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Virginia Tech

Computer Vision Tracking of sUAS From a Pan/Tilt Platform

Abstract

dc:description.abstract

The 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.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Aerospace Engineering
Department dc:contributor.department
Aerospace and Ocean Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ogorzalek, Jeremy Patrick
Chair dc:contributor.committeechair
  • Black, Jonathan T.
Committee members dc:contributor.committeemember
  • England, Scott L.
  • Psiaki, Mark L.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:21173
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/90578

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Ogorzalek, Jeremy Patrick. Computer Vision Tracking of sUAS From a Pan/Tilt Platform. masters thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90578