Assessment of Vision-Based Target Detection and Classification Solutions Using an Indoor Aerial ...

111 Pages·2014·1.46 MB·English

NAVAL
POSTGRADUATE
SCHOOL
MONTEREY, CALIFORNIA
THESIS
ASSESSMENTOFVISION-BASEDTARGETDETECTION
ANDCLASSIFICATIONSOLUTIONSUSINGANINDOOR
AERIALROBOT
by
NicoleR.Ramos
September2014
ThesisAdvisor: TimothyH.Chung
SecondReader: OlegA.Yakimenko
Approvedforpublicrelease;distributionisunlimited
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09-30-2014 Master’sThesis 09-01-2012to09-01-2014
4.TITLEANDSUBTITLE 5.FUNDINGNUMBERS
ASSESSMENTOFVISION-BASEDTARGETDETECTIONANDCLASSIFICATION
SOLUTIONSUSINGANINDOORAERIALROBOT
6.AUTHOR(S)
NicoleR.Ramos
7.PERFORMINGORGANIZATIONNAME(S)ANDADDRESS(ES) 8.PERFORMINGORGANIZATIONREPORT
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NavalPostgraduateSchool
Monterey,CA93943
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The views expressed in this document are those of the author and do not reflect the official policy or position of the Department of
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13.ABSTRACT (maximum200words)
Theroleofunmannedaerialvehicles(UAVs)inmilitary,commercialandrecreationalapplicationsiscontinuouslyevolvingasdevel-
opmentsintechnologyincreasecapabilities.Theresearchhereinpresentsaninexpensivecomputer-vision-basedsolutionfordetection
andclassificationofastationarytargetwithamobileaerialsensorasaprototypingplatform.Themaingoalofthissystemistouse
commercial-off-the-shelfandopen-sourcecomponentstoreducedesigncomplexitytoprovidealegacyproductforfuturedevelop-
mentofspecificcapabilities.Colorimagerycollectedduringflightusingalow-resolutioncameraisusedtotesttheapplicationofa
simplealgorithmagainstacommerciallyavailableandlowcostsensor.Originalimageprocessingalgorithmsthatleveragetheexisting
bodyofworksintheopen-sourcecommunityaredevelopedandtestedwithintheSystemsEngineeringconstruct.Systemarchitec-
tureleveragesamodularapproachthatcanbeeasilymodifiedandadaptedtochangingrequirementsandobjectives.Conclusionsare
drawnandrecommendationsforfurtherstudyandsystemdevelopmentarepresented.
14.SUBJECT TERMS UAV, computer-vision, detection and classification, mobile aerial sensor, systems engineering 15.NUMBEROF
PAGES 111
16.PRICECODE
17.SECURITY 18.SECURITY 19.SECURITY 20.LIMITATIONOF
CLASSIFICATIONOF CLASSIFICATIONOFTHIS CLASSIFICATIONOF ABSTRACT
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Approvedforpublicrelease;distributionisunlimited
ASSESSMENTOFVISION-BASEDTARGETDETECTIONAND
CLASSIFICATIONSOLUTIONSUSINGANINDOORAERIALROBOT
NicoleR.Ramos
Lieutenant,UnitedStatesNavy
B.S.,UnitedStatesNavalAcademy,2008
Submittedinpartialfulfillmentofthe
requirementsforthedegreeof
MASTEROFSCIENCEINSYSTEMSENGINEERING
fromthe
NAVALPOSTGRADUATESCHOOL
September2014
Author: NicoleR.Ramos
Approvedby: TimothyH.Chung
ThesisAdvisor
OlegA.Yakimenko
SecondReader
CliffordA.Whitcomb
Chair,DepartmentofSystemsEngineering
iii
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ABSTRACT
The role of unmanned aerial vehicles (UAVs) in military, commercial and recreational ap-
plications is continuously evolving as developments in technology increase capabilities.
The research herein presents an inexpensive computer-vision-based solution for detection
and classification of a stationary target with a mobile aerial sensor as a prototyping plat-
form. The main goal of this system is to use commercial-off-the-shelf and open-source
components to reduce design complexity to provide a legacy product for future develop-
ment of specific capabilities. Color imagery collected during flight using a low-resolution
camera is used to test the application of a simple algorithm against a commercially avail-
able and low cost sensor. Original image processing algorithms that leverage the existing
bodyofworksintheopen-sourcecommunityaredevelopedandtestedwithintheSystems
Engineering construct. System architecture leverages a modular approach that can be eas-
ily modified and adapted to changing requirements and objectives. Conclusions are drawn
andrecommendationsforfurtherstudyandsystemdevelopmentarepresented.
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Table of Contents
1 Introduction 1
1.1 MotivationforResearch . . . . . . . . . . . . . . . . . . . . . 1
1.2 DesiredCapabilities . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 ScopeofThesis . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5 MainContributions . . . . . . . . . . . . . . . . . . . . . . . 12
1.6 OrganizationoftheThesis. . . . . . . . . . . . . . . . . . . . . 13
2 SystemArchitectureandModelFormulation 15
2.1 LaboratoryScenarioDescription . . . . . . . . . . . . . . . . . . 15
2.2 FunctionalArchitecture. . . . . . . . . . . . . . . . . . . . . . 20
2.3 PhysicalArchitecture. . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 ComputerVisionandPerception . . . . . . . . . . . . . . . . . . 27
3 SystemDevelopmentandIntegration 31
3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 PerceptionAlgorithmImplementation . . . . . . . . . . . . . . . . 33
3.3 DescriptionofSoftwareImplementation . . . . . . . . . . . . . . . 36
3.4 ExpectedResults . . . . . . . . . . . . . . . . . . . . . . . . 49
4 ExperimentalResults 53
4.1 MethodologyforAnalysis. . . . . . . . . . . . . . . . . . . . . 53
4.2 PerceptionAlgorithmResults . . . . . . . . . . . . . . . . . . . 57
5 Conclusions 71
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 LessonsLearnedandShort-TermRecommendations . . . . . . . . . . 71
5.3 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . 73
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Appendix:AssociatedProgramsandPythonCode 77
References 79
InitialDistributionList 87
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