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dc.contributor.authorKirchhoff, Allan Richarden
dc.date.accessioned2015-05-23T08:06:00Zen
dc.date.available2015-05-23T08:06:00Zen
dc.date.issued2014-10-16en
dc.identifier.othervt_gsexam:3492en
dc.identifier.urihttp://hdl.handle.net/10919/52569en
dc.description.abstractUnmanned ground vehicles (UGVs) are increasingly being used for civilian and military applications. Passive sensing, such as visible cameras, are being used for navigation and object detection. An additional object of interest in many environments is text. Text information can supplement the autonomy of unmanned ground vehicles. Text most often appears in the environment in the form of road signs and storefront signs. Road hazard information, unmapped route detours and traffic information are available to human drivers through road signs. Premade road maps lack these traffic details, but with text localization the vehicle could fill the information gaps. Leading text localization algorithms achieve ~60% accuracy; however, practical applications are cited to require at least 80% accuracy [49]. The goal of this thesis is to test existing text localization algorithms against challenging scenes, identify the best candidate and optimize it for scenes a UGV would encounter. Promising text localization methods were tested against a custom dataset created to best represent scenes a UGV would encounter. The dataset includes road signs and storefront signs against complex background. The methods tested were adaptive thresholding, the stroke filter and the stroke width transform. A temporal tracking proof of concept was also tested. It tracked text through a series of frames in order to reduce false positives. Best results were obtained using the stroke width transform with temporal tracking which achieved an accuracy of 79%. That level of performance approaches requirements for use in practical applications. Without temporal tracking the stroke width transform yielded an accuracy of 46%. The runtime was 8.9 seconds per image, which is 44.5 times slower than necessary for real-time object tracking. Converting the MATLAB code to C++ and running the text localization on a GPU could provide the necessary speedupen
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttext localizationen
dc.subjectOptical Character Recognitionen
dc.subjectUnmanned Ground Vehiclesen
dc.subjectStroke Width Transformen
dc.subjectStroke Filteren
dc.subjectAdaptive Thresholdingen
dc.subjectMachine Visionen
dc.subjectRobotic Perceptionen
dc.subjectMachine Learningen
dc.subjectSupport Vector Machineen
dc.titleText Localization for Unmanned Ground Vehiclesen
dc.typeThesisen
dc.contributor.departmentMechanical Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineMechanical Engineeringen
dc.contributor.committeechairWicks, Alfred L.en
dc.contributor.committeememberParikh, Devien
dc.contributor.committeememberBird, John P.en


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