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dc.contributor.authorKirchhoff, Allan Richarden_US
dc.date.accessioned2015-05-23T08:06:00Z
dc.date.available2015-05-23T08:06:00Z
dc.date.issued2014-10-16en_US
dc.identifier.othervt_gsexam:3492en_US
dc.identifier.urihttp://hdl.handle.net/10919/52569
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_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjecttext localizationen_US
dc.subjectOptical Character Recognitionen_US
dc.subjectUnmanned Ground Vehiclesen_US
dc.subjectStroke Width Transformen_US
dc.subjectStroke Filteren_US
dc.subjectAdaptive Thresholdingen_US
dc.subjectMachine Visionen_US
dc.subjectRobotic Perceptionen_US
dc.subjectMachine Learningen_US
dc.subjectSupport Vector Machineen_US
dc.titleText Localization for Unmanned Ground Vehiclesen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairWicks, Alfred L.en_US
dc.contributor.committeememberParikh, Devien_US
dc.contributor.committeememberBird, John P.en_US


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