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    Chapter 16

    Omnidirectional Human Intrusion Detection System Using Computer Vision Techniques

    Wai Kit Wong, Chu Kiong Loo, and Way Soong Lim

    Contents16.1 Introduction....................................................................................................................41216.2 HumanIntruderSurveillanceSystem.............................................................................414

    16.2.1BurglarAlarmSystem..........................................................................................41416.2.1.1PassiveInfraredMotionDetectorSystem..............................................41516.2.1.2UltrasonicMotionDetectorSystem......................................................41516.2.1.3Glass-BreakDetectorSystem.................................................................41616.2.1.4PhotoelectricBeamSystems...................................................................41716.2.1.5VibrationSensorSystem........................................................................41716.2.1.6PassiveMagneticFieldDetectionSystem...............................................41716.2.1.7MicrophonicDetectionSystem..............................................................41816.2.1.8TautWirePerimeterSecuritySystem.....................................................418

    16.2.2Radar-BasedHumanIntruderDetectionSystem.................................................41916.2.3 ImageProcessing-BasedHumanIntruderDetectionSystem.............................. 420

    16.2.3.1VisionSpectrumImageProcessing-BasedHumanIntruderDetectionSystem.......................................................421

    16.2.3.2NightVision/InfraredSpectrumImageProcessing-BasedTrespasserDetectionSystem.................................................................................. 423

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  • 412 Effective Surveillance for Homeland Security

    16.1 IntroductionHomelandsecurityisaneffortbygovernment(normallyparkedundernationaldefensedepart-ment)topreventterroristattack inacountryandreduceacountrysvulnerabilitytoterrorism[1].Thescopesofhomelandsecurityonhumantrespasserdoincludetheprotectionofacriticalinfrastructuresperimeterandthebordersecurity(countryborderofterritorialland,water,andairspace).Homelandthreatsrefertothecrimesthathaveanimmediateandvisibleimpactonthelocalcommunityandaffectcitizenqualityoflife.Inthefaceofunknownfutureterroristthreats,illegal immigrants that will flush out a peaceful and economically stable country as refugees,whichmightbringinthefts,smugglers,etc.,issues,however,nationalsecuritydepartmentandconvergentsecurityengineerswillhavetodevelopstrategiesandsecurity/surveillancesystemtofulfilltherequirementofhomelandsecurity,ontrespassersthreats.Thischapterproposessomehomelandsecuritysystemsonhumanintruderdetection.

    Intrusiondetectionistheactofdetectingatrespasserinaguardzone.Humanintrusiondetec-tionsystemisasystemusedtodetecthumantrespasserenteringaprohibitedarea.Conventionalhumanintrusiondetectionsystemusesburglaralarmsystem(activeorpassivesensors),wherebymodernhumanintrusiondetectionsystemappliescomputervisiontechniques,bothtotraceoutwhetherthereisanexistenceofatrespasser/humanbeingornotinaprohibitedarea.Themainmeritofmodernhumanintrusiondetectionsystemascomparedtoconventionalhumanintru-siondetectionsystemisthattheimageprocessing-basedhumanintrusiondetectionsystemcanhelpcapturepictures.Sincepicturesarecaptured,therearemorechancesoftheintrudersbeingrecognizedandcaught.Theauthoritycanwiselyplacesecuritycamerasineveryvulnerableplaceoftheguardedarea,indoorandoutdoor,thatwouldbeaccessibletoahumanintruder.Thisallowstheauthoritytostaysafeinsidethepremisewhilestillbeingabletoseewhatishappeningintheoutdoorareaofthepremise.Italsogivesthemmoretimetocallforhelporbackupiftheynoticeanysecuritythreat.

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    16.2.4DirectionalversusOmnidirectionalViewing...................................................... 42316.2.4.1OpticalApproachversusMechanicalApproach................................... 42416.2.4.2VisionSpectrum-BasedOmnidirectionalSurveillanceSystem............. 42716.2.4.3Thermal/InfraredSpectrum-BasedOmnidirectional

    SurveillanceSystem.............................................................................. 42816.3 UnwarpingMethods.......................................................................................................431

    16.3.1DiscreteGeometryTechniqueMethod............................................................... 43216.3.2Pano-MappingTableMethod............................................................................. 43316.3.3Log-PolarMappingMethod................................................................................43516.3.4PerformanceEvaluation...................................................................................... 436

    16.4 AutomaticHumanIntruderDetectionAlgorithm......................................................... 43916.4.1PartitionedRegionofInterestAlgorithm............................................................ 43916.4.2HumanHeadCurveTestAlgorithm.................................................................. 44116.4.3ExperimentalResults.......................................................................................... 447

    16.4.3.1ExperimentalResultsforPartitionedROI-BasedHumanIntruderDetectionAlgorithm................................................. 448

    16.4.3.2ExperimentalResultsforHumanHeadCurveTestAlgorithm.............45016.4.4ComparisonbetweenTwoProposedHumanIntruderDetectionAlgorithms.....452

    16.5 ConclusionandFutureResearchDirections...................................................................453References................................................................................................................................454

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    Ingeneral,themoderncomputervisiontechnique-basedhumanintrusiondetectionsystemcanbedividedintotwomaincategories:oneisvisionspectrum-basedhumanintrusiondetectionsystemandanotheroneisnightvision/infrared(IR)spectrum-basedhumanintrusiondetectionsystem.Visionspectrum-basedhumanintrusiondetectionsystemappliesvisionspectrumrangeimagingtoolstocaptureimages.Oneproblemencounteredinthistypeofsurveillancesystemisthechangeinambientlight,especiallyinanoutdoorenvironmentwherethelightingconditionvariesnaturally.Thismakestheconventionaldigitalcolorimagesanalysistaskinsmartsurveil-lancesystembecomeverydifficult.Onecommonapproachtoeliminatethisproblemistotrainthesystemtocompensateforanychangeintheillumination[2].However,thisisgenerallynotenoughforhumanintrusioninthedark.Itisbettertoapplysomesortofnightvisionimagingtoolthathelpsinimagingobjectsinthedark.ThencomestheapplicationofIRspectrum-basedhumanintrusiondetectionsystem.Thermalcameraisanexcellentnightvisionsecuritycamera.ItperceivesIRradiationanddoesnotneedasourceofillumination.Thermalcameraisidealforanylow-lightareas,notjustforthenighttime.Itproducesanimageinthedarkestofnightsandcanviewthroughlightfog,rain,andsmoke.Thermalimagingcamerasmakesmalltemperaturedifferencesvisible.Thermalimagingcamerasarecurrentlyappliedwidelyinmanyneworexistingsecuritynetworks.

    Ifa single imagingtool is tomonitora singleangleofa location, thenformore locationsindifferentanglesofview,more imagingtoolsarerequired.Hence, itwillcostmore,besidescomplicatingthesurveillancenetwork.Therefore,anomnidirectionalhumanintrusiondetec-tionsystemusingminimumhardwareisdevelopedtoovercomethecostandnetworkcomplica-tionproblems.Themethodappliedtoobtainomnidirectionalimagescanbeclassifiedintotwoapproaches[3]:(1)mechanicalapproachand(2)opticalapproach.Sincemechanicalapproachleads to many problems on discontinuity and inconsistency, therefore, optical approach wasfavoredbypractitioners.

    Thecapturedomnidirectionalimagesnormallyhavesomedifferentpropertiescomparingtoperspectiveimagesintermsofimagingdeformation.Suchdistortionleadstotheimagesbeingdif-ficulttobedirectlyimplemented.Thus,itisnecessarytoworkoutanefficientmethodtounwarptheomni-image.Unwarping,generally,isamethodusedindigitalimageprocessinginopeningup an omnidirectional image into a panoramic image, making the information on the imagetobeablefordirectimplementation.Unwarpingmethodisactivelyadoptedintheapplicationofvisual surveillance systems.Thereare currently threeunwarpingmethods activelypracticedaroundtheworld,whicharethepano-mappingtablemethod[4],discretegeometrytechniques(DGT)method[5],andlog-polarmappingmethod[6].Thischapterstudiestheadvantagesanddisadvantagesofeachmethod,andtheirperformanceiscomparedandevaluated.

    Conventionalsurveillancesystemnormallyemployshumanobserverstoanalyzethesurveil-lancevideo.Sometimesthisismorepronetoerrorduetolapsesinattentionofthehumanobserver[7].Itisafactthatahumansvisualattentiondropsbelowacceptablelevelswhenassignedtovisualmonitoring,andthisfactholdstrueevenforatrainedpersonnel[8,9].Theweaknessinconven-tionalsurveillancesystemhasraisedtheneedforasmartsurveillancesystemwhereitemployscomputerandpatternrecognitiontechniquestoanalyzeinformationfromsituatedimagingtoolsandautomaticallydetectatrespasser[10].Twoautomatichumanintrusiondetectionalgorithmsarediscussed in this chapter; this includes partitioned regionof interest (ROI) algorithm [11]andhumanheadcurvetestalgorithm[12,13].Withthealgorithmsproposedinthischapter,itissimpletodetectthehumanintrusionofmorethanonelocationinasingleviewcapturedbytheimagingtool.Thesemonitoringandsubsequentanalysesoftheimagesfromtheinspectioncanalertsecuritypersonneltotakefurtheractiontoeithercatchorhustlethetrespassereffectively.

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    Inthischapter,thefundamentalsofhumanintrusiondetectionsystem,classicalburglaralarmsystem(activeorpassivesensorsystem)andradarsystemversuscomputervisiontechniquesystem,visionspectrumimagingversusIRimagingsystem,anddirectionalversusomnidirectionalview-ing,arefirstdiscussed.Thealgorithmandimplementationofsomeuniversalunwarpingmethodswill be discussed too, such as discrete geometric transforms (DGTs) [4], pano-mapping tablemethod[5],andlog-polarmappingmethod[6]proposedintransformingthecapturedomnidi-rectionalimagesintopanoramicform,providingobserverorimageprocessingtoolsawideangleof view.Besides that, automatic human intrusiondetection is implemented in the omnidirec-tionalimagingsystems(bothinvisionspectrumandinIRspectrum,respectively).ThedevelopedhumanintrusionalgorithmsarepartitionedROIalgorithm[11]andhumanheadcurvetestalgo-rithm[12,13],andtheirdesignprocedureswillbeincludedhere.Later,someexperimentalresultstoprovethealgorithmsproposedforthehumanintrusiondetectionsystemareshown.Inthelastsectionofthischapter,wesummarizetheworkandenvysomefutureenhancement.

    16.2 Human Intruder Surveillance SystemAccording to tort law,property law,andcriminal law[14], ahuman intruder is apersonwhocommitstheactoftrespassing/intrudingonaprohibitedarea,thatis,withoutthepermissionoftheauthority.Ahumanintrudertrespassestoacriticalinfrastructuresperimeterandthebordersecurity isdefinedas an intentional interferencewith the infringeontonational security thatproximatelywillcauseinjury,vandalism,terrorism,theft,etc.InUnitedKingdomjurisdictions,trespassing has been codified to clearly define the scope of the remedy, and in most jurisdic-tions,trespassingremainsapurelycommonlawremedy,thescopeofwhichvariesbyjurisdiction.Surveillanceisthemonitoringoftheactivities,behavior,orotherchanginginformation,normallywithpeopleinasurreptitiousmannerandattheentranceofprohibitedarea.Surveillanceisveryusefultosecurityauthoritytorecognizeandmonitorthreatsandpreventcriminalactivity.

    Humanintrudersurveillancesystemcanbeusedtohelpsecurityauthorityguardacriticalinfrastructuresperimeterandthebordersecurity.Itisdesignedtodetectanintrusion,activateawarningdeviceupondetectionofanintrusion,determinecrime,protectlifeandproperty,bringanappropriate response toanemergency,andenhance theapprehensionofcriminals.Humanintruder surveillance systemcanbedivided into threemaincategories,whichare theconven-tionalburglar alarmsystem, the radar-basedhuman intruderdetection system, and the imageprocessing-basedhumanintruderdetectionsystem.

    16.2.1 Burglar Alarm SystemBurglar(orintrusion)alarmsystemsareelectronicalarmsdesignedtoalerttheusertoaspecificintruder.DetectionsensorsareconnectedtoacontrolunitviaanarrowbandRFsignalorlow-voltagewiringthatisusedtocommunicatewitharesponsedevice.Newconstructionsystemsare predominately hardwired for efficient, more economical hardware installation. Refurbishconstructionoftenapplieswirelesssystemsforafaster,moreeconomicalchannelinstallation,duetoneednotdiggingwall,ceiling,andfloorforrewiring.Somesystemsserveasinglepur-poseofeitherburglarorfireprotectionandsomecombinationsystemsprovidebothfireandintrusion protections. Systems range from small, self-contained noisemakers to complicated,multi-zonedsystemswithcolor-codedcomputermonitoroutputs.Manyoftheseburglaralarmsystemconceptsalsoapplytoportablealarmsystemsforprotectingmotorvehicles(cars,trucks,

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    busses,etc.)andtheircontents.Burglaralarmsystems(orintrusiondetectionsystems,perimeterdetection systems, perimeter security systems, perimeter protection systems, andmanymoretermsfortheidenticalitem)aredividedintomanytypes,suchaspassiveIR(PIR)motiondetec-torsystem,ultrasonicmotiondetectorsystem,glass-breakdetectorsystem,photoelectricbeamsystem,vibrationsensorsystem,passivemagneticfielddetectionsystem,microphonicsystem,and taut wire perimeter security system. Each of these burglar alarm systems will be brieflyillustratedinthefollowing.

    16.2.1.1 Passive Infrared Motion Detector System

    APIRsensor isanelectronicsensorthatmeasuresIRlightradiatingfromobjectswithinitsfieldofview.PIRsensorsareoftenusedintheconstructionofPIR-basedmotiondetectors.ThePIR-basedmotiondetectorisoneofthemostcommondetectorsfoundinhouseholdandsmallbusinessenvironmentsbecauseitoffersaffordableandreliablefunctionality.Thetermpassivemeansthedetectorisabletofunctionwithouttheneedtogenerateandradiateitsownenergy.Thisisdifferentfromultrasonicandmicrowavevolumetricintrusiondetectorsinwhichtheyareactive inoperation.IfanIR-emittingobjectexists inthecoveragearea, thePIR-basedmotiondetectorisabletoidentifybyfirstlearningtheambienttemperatureofthemonitoredspaceandthendetectingachangeinthetemperaturecausedbythepresenceofthatobject.Applyingthedifferentiationprinciple(creatingindividualzonesofdetectionwhereeachzonecomprisesoneormorelayers)canachievedifferentiation.Betweenthezones,thereareareasofnosensitivity(deadzones)thatareusedbythesensorforcomparison,thatis,acheckofpres-enceornon-presence;PIR-basedmotiondetectorcanverifywhetheranintruderorobjectisactuallyinplace.

    InaPIR-basedmotiondetector,thePIRsensoristypicallymountedonaprintedcircuitboardcomprisingtherequiredelectronicsusedtointerpretthesignalsfromthepyroelectricsensorchip[15].Thecompleteassemblageisconfinedwithinahousingattachedinasitewherethesensorcanviewtheareatobemonitored.IRenergyisabletoreachthepyroelectricsensorthroughthewin-dowbecausetheplasticusedistransparenttoIRradiationbutsomehowtranslucenttovisiblelightspectrum.Thisplasticsheetalsoinhibitstheintrusionofdustand/orinsectsfromobscuringthesensorsfieldofviewand,inthecaseofinsects,fromgeneratingfalsealarms.SomemechanismshavebeenusedtoconcentratethedistantIRenergyontothesensorsurface.

    ThePIR-basedmotiondetectorworkingasahumanintruderdetectionsystemhasthemeritsofsimpleandlowerinstallationcostandlesssensitivetoilluminationchanges.However,PIR-basedmotiondetectorhasthesedemeritswhenworkingasahumanintruderdetectionsystem:(1)itcanbeeasilytriggeredbymovinganimals,blowingshrubs,etc.;(2)itcannotdetectpeoplewhoarestationary,thusmayleadtoalargenumberoffalsealarms;(3)itsoutputishighlybursty(somecommercialoff-the-shelfsensorsuseaheuristicsolutiontomakeupforthis,byignoringdetectionsthatfallwithinarefractoryperiodofanearlierevent.Theseissuesarelargelyignoredby thevastmajorityofPIR-basedresearchby limiting their systemtosingle-personscenariosand/orassumingpeoplearealwaysmoving);and(4)itdoesnottoleratelargeareasorlargetem-peraturechanges.

    16.2.1.2 Ultrasonic Motion Detector System

    Thetransmitterof theultrasonicdetector is radiating anultrasonic signal into the areaundersurveillance.Theultrasonicsoundwavesarereflectedbysolidobjects(suchasthesurrounding

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    walls,floor,andceiling)andthendetectedbythereceiver.Sinceultrasonicwavesaretransmit-ted through air, the hard-surfaced objects tend to reflect most of the ultrasonic energy, whilesoftsurfacestendtoabsorbmostenergy.Thereceivedfrequencywillbeequaltothetransmittedfrequencywhenthesurfacesarestationary.However,achangeinfrequencywilloccurasaresultoftheDopplerprinciple,duetoapersonorobjectmovingtowardorawayfromthedetector.Thiseventwillinitiateanalarmsignal.

    Ingeneral,ultrasonicmotiondetectorcanbecategorizedintotwotypes:activeandpassive.Activeultrasonicmotiondetectoremitsultrasonicsoundsthatareinaudibletohumanear(fre-quenciesbetween15and75kHz).Passiveultrasonicmotiondetectorconsistsofonlyreceiversthatsimplyreceivetheemittedsounds.Asthesedevicesareoneofthemostsensitiveamongthehumanintruderdetectionsystems,theyarealsoexpensiveincost.

    Ultrasonicmotiondetector systemsutilizeadvanced technology,but somehowunder someconditions,theyarepronetofalsealarmsbystuffslikepassingbirdsorinsects,gustsofwind,orvibrationscausedbyairplanespassingoverhead.Passiveultrasonicmotiondetector systemsdonotprovidecompletedetectioninareaswithlargeobjects,thuscreatingadeadzone.Hence,inthesecases,anothertypeofdetectionsystemmayberequiredtoworktogethertheultrasoundsystemasaseconddetectorformoreaccuratealarm.Duetoitspooreffectiveness,thistechnologyisconsideredobsoletebymanyalarmprofessionalsandisnotactivelyinstalled.

    16.2.1.3 Glass-Break Detector System

    Aglass-breakdetectorisasensorusedinelectronicburglaralarmsystemsfordetectingifthereisapaneofglassshatteredorbroken.Thesedetectorsarecommonlyplacednearglassdoorsorglassstorefrontwindowstodetectwhetherthereisanintrudertryingtobreaktheglassandenterthepremises.Glass-breakdetectorsnormallyapplyamicrophone,whichmonitorsanynoiseorvibra-tionscomingfromtheglass.Ifthevibrationsexceedacertainthreshold(userselectable/preset),theyareanalyzedbydetectorcircuitry.Simplerdetectorsjustsimplyapplynarrowbandmicrophonesthattunedtofrequenciestypicalofglassshatteringandreacttosoundabovecertainthreshold,whereasmorecomplexdesignswillcomparethesoundanalysistooneormoreglass-breakprofilesusingsignaltransformssuchasdiscretecosinetransformorfastFouriertransformandreactifboththeamplitudethresholdandstatisticallyexpressedsimilaritythresholdarebreached.

    Theglass-breakdetectorcanbeappliedforinternalperimeterbuildingprotection.Whenglassbreaks, itactuallycreates sound inawidebandof frequencies ranging frominfrasonic (below20Hz, this frequency range is inaudible tohumanear) to theaudioband (20Hz to20kHzthatisaudibletohumanear)rightuptoultrasonic(whichisabove20kHzandagainitfallsinrangeinaudibletohumanear).Therearetwotypesofglass-breakdetectorsingeneral:glass-breakacousticdetectorandseismicglass-breakdetector.Glass-breakacousticdetectorsaremountedincloseproximitytotheglasspanesandlistenforsoundfrequenciesassociatedwithglassbreaking.Seismicglass-breakdetectorsaredifferentinthattheyareinstalledontheglasspane.Whenglassbreaks,itproducesspecificshockfrequenciesthattravelthroughtheglassandoftenthroughthewindow frame and the surrounding walls and ceiling. Typically, the most intense frequenciesgeneratedarebetween3and5kHz,dependingonthetypeofglassandthepresenceofaplasticinterlayer.Seismicglass-breakdetectorssensetheseshockfrequenciesandgenerateanalarmcon-ditionaccordingly.

    However,glass-breakdetectorscanonlybeappliedatlimitedareas,forexample,building/premiseswithwindows.Glass-breakdetectorsarealsosensitivetoenvironmentaleffect,suchas

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    waterspreading/rain,insects,birds,orobjecthittingwindows(butwindownotclashing)andcansometimesgeneratefalsealarm.

    16.2.1.4 Photoelectric Beam Systems

    PhotoelectricbeamsystemsdetectthepresenceofanintruderbytransmittingvisibleorIRlightbeamsacrossanarea,wherethesebeamsmaybeobstructed.PhotoelectricbeamsensorstransmitabeamofIRlighttoaremotereceivercreatinganelectronicfence.Thesesensorsareoftenusedtocoveropeningssuchasdoorwaysorhallways,actingessentiallyasatripwire.Oncethebeamisbroken/interrupted,analarmsignalisgenerated.Photoelectricbeamsystemconsistsoftwomaincomponents:atransmitterandareceiver.Thetransmitterusesalight-emittingdiode(LED)asalightsourceandtransmitsaconsistentIRbeamoflighttoareceiver.Thereceiverconsistsofaphotoelectriccellthatdetectswhenthebeamispresent.Ifthephotoelectriccellfailstoreceiveatleast90%ofthetransmittedsignalforasbriefas75ms(timeofanintrudercrossingthebeam),analarmsignalisgenerated.

    Thephotoelectricbeamsystemworkingasatrespasserdetectionsystemhasthemeritsofeasyinstallationandhighimmunitytoambientlight,anditsfunctionalityisnotaffectedbyelectricalandmagneticfields.However,photoelectricbeamsystemhasthesedemeritswhenconsideredtobeworkedasatrespasserdetectionsystem:(1)highinstallationcostwithtwodeviceshavingtobemounted,wired,andadjustedand(2)detectionofverysmallobjects;thismaysomehowleadtoalargenumberoffalsealarmsduetoanimals,passingobjectsblownbywinds,orevenpassinginsectstriggeringanalarm.

    16.2.1.5 Vibration Sensor System

    Vibrationsensorsrelyonanunstablemechanicalconfigurationthatformspartoftheelectricalcircuit.Theworkingoperationforvibrationsensoristhatwhenmovementorvibrationoccurs,theunstableportionofthecircuitmovesandbreaksthecurrentflow;thisleadstoanalarm.Thetech-nologyofthedevicesisvaryingandcanbesensitivetodifferentlevelsofvibration.Themediumtransmittingthevibrationmustbecorrectlyselectedforthespecificsensorsastheyarebestsuitedtodifferenttypesofstructuresandconfigurations.

    Vibrationsensorsareveryreliablesensors.Itgenerateslowfalsealarmrateintrespasserdetec-tionsystemandmoderateinpricerange.However,thistypeofdetectionsystemmustalwaysbefencemounted.Also,vibrationsensorsareanewtechnologywithanunprovenrecordasopposedtothemechanicalsensor,whichinsomecaseshasafieldrecordinexcessof20years.Thatswhyitisnotwidelyseeninthemarketyet.

    16.2.1.6 Passive Magnetic Field Detection System

    Thistypeofburiedsecuritysystemisbasedonthemagneticanomalydetection(MAD)principleofoperation.TheprincipleoftheMADisbasedontheabilitytosensetheanomalyintheEarthmagneticfieldproducedbythetarget[16].Thesystemappliesanelectromagneticfieldgeneratorpoweredbytwowiresrunninginparallel.Bothwiresrunalongtheperimeterandareusuallyinstalledabout5in.apartontopofawallorabout12in./30cmbelowground.Thewiresareconnectedtoasignalprocessorthatanalyzesanychangeinthemagneticfield.Passivemagnetic

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    fielddetectionsystemhasverylowfalsealarmrate.Itcanbeputontopofanywallandhasveryhighchanceofdetectingrealburglars.However,itishavinghighinterferenceifitisinstallednearhigh-voltagelinesorradars.

    16.2.1.7 Microphonic Detection System

    Microphonic-baseddetectionsystemshaveavarietyofdesign,butallaregenerallybasedonthedetectionof a trespasser attempting tocutor climbover a chainwire fence.Themicrophonicdetectionsystemsareusuallyinstalledassensorcablesattachedtorigidchainwirefences.Oneexampleisthemicrophonicfencedisturbancesensorsystem.Microphonicfencedisturbancesen-sorsapplythesignalsgeneratedbytheminuteflexingoftriboelectriccoaxialsensorcable,whichareanalyzedbypowerfulsignalprocessorstodetectthesoundassociatedwithcutting,climbing,orliftingthefencestructure.Thesystemscanalsobeembeddedwithaspecialaudiochannelthatenablessecuritiestolistentoactivityalongeachzoneofthefencefortheprotectionofexistingfencesandstructuresagainstcutting,climbing,orlifting.Theycanalsobefittedtocoiledrazorwirefences.

    Inoperation,microphonicfencedisturbancesensorsystemsaredesignedtodetectandanalyzeincomingelectronicsignalsreceivedfromthesensorcableandthentogeneratealarmsfromsig-nalsthatexceedsomepresetconditions.Thesystemsofferadjustableelectronicstoallowinstallerstochangethesensitivityofthedetectorsalarmtosuitaspecificenvironmentalcondition.Thetuningofthesystemisnormallyaccomplishedbeforethedetectiondevicesareputincommis-sioning.Microphonicfencedisturbancesensorsystemsareverycheapincost,easytoinstall,andsimpleinconfiguration.However,thesesystemshaveahighrateoffalsealarmsbecausesomeofthesesensorsmightbetoosensitivetoextremeweather,contactby largeanimals,badlymain-tainedfences,andovergrownvegetation.

    16.2.1.8 Taut Wire Perimeter Security System

    Atautwireperimetersecuritysystemisnormallyastreamoftensionedtripwiresusuallymountedonawallorfence.Itisparticularlyusefulfordetectingclimbing(ontopofawall)orwhereitisnecessarytobuildupaphysicalbarrier(fence).Thissystemisdesignedtodetectanyphysicalattempttopenetratethebarrier.

    Tautwireperimetersecuritysystemcanoperatewithavarietyofdetectorsorswitchesthatdetectmovementateachendofthetensionedwires.Thesedetectorsorswitchescanbeanelec-tronicstraingauge,astaticforcetransducer,orasimplemechanicalcontact.Falsealarmscausedbybirds and animals canbe avoidedby tuning thedetectors to omit objects that exert smallamountsofpressureonthewires.However,thistypeofsystemisvulnerabletotrespassersdig-gingunderthefence.Hence,aconcretefootingisinstalleddirectlybelowthefencetopreventsuchtrespassing.Tautwireperimetersecuritysystemsarehavingveryreliablesensors,lowrateoffalsealarms,andhighrateofdetection.However,thistypeoftrespasserdetectionsystemisveryexpensive,itiscomplicatedtoinstall,andthetechnologyisquiteancient.

    Ingeneral,conventionalburglaralarmsystemsaresimpleandlowerininstallationandmain-tenancecost.However,theyhavealowerprobabilityofdetectinghumanintrudersandhighfalsealarmrate.Thisisduelargelytomanyuncontrollablefactors,suchasenvironmentalissues(rain,ice, wind, standing water), random animals, and human activities, as well as other electronicinterferencesources.

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    16.2.2 Radar-Based Human Intruder Detection SystemThesecondcategoryofhumanintrudersurveillancesystemisradar-basedhumanintruderdetec-tionsystem.Radarisradiodetectionandranging,whichisanobjectdetectionsystemthatappliesradiowavestodeterminetherange,attitude,direction,andspeedofanobject.Inhumanintru-siondetectionsystem,radarcanbeusedtodetecthumanintruder.Theradardishorantennatransmitspulsesof radiowavesormicrowave thatwill bounceoffanyobject in theirpath.Aradarscomponentconsistsof(1)atransmitterthatgeneratestheradiofrequencysignalwithanoscillatorandcontrolsitsdurationbyamodulator,(2)awaveguidethatbondsthetransmitterandantenna,(3)aduplexerthatactsastheswitchamongtheantennaandtransmitterorthereceiverforthesignalwhentheantennaisusedinbothsituations,(4)areceiverthatknowstheshapeofthedesiredreceivedsignal(so-calledapulse),and(5)anelectronicsectionthatcontrolsallthosedevicesandtheantennatoperformtheradarscanorderedbysoftware.

    For theworkingprinciplesof radar, the transmitterwill emit radiowaves (radar signal) inpredetermineddirections.Whenthesesignalscomeincontactwithanobject,theyusuallyreflect/scatterinmorethanonedirection.Theradarsignalsarereflectedbacktowardthetransmitter.Inhumandetection(movingobjectdetection),iftheobjectismovingeithercloserorfartheraway,thereisaslightchangeinthefrequencyoftheradiowaves.Dopplerradarisoneofsuchcommonperimetermonitoringsystems.However,thiskindofradarsystemrequiredthecoverageareatobeclearoffoliageandobstaclesthatmightcreatecoverageshadowsandfalsealarm.Thisrequirementmightnot suitmanyoutdoorenvironments,andeven though in indoorusage, itmightcreateundesirableinstallationandmaintenanceexpenses.Also,slow-movingtargetssometimesmightnotbedetectedonthisradarsystemduetolow-resolutiondetectableDopplershift[17].InRef.[17],engineersareprovingthattheultra-wideband(UWB)RFcanovercomethedeficienciesonconventionalDopplerradar.Someotherrecentadvancedradarsystemsalsodevelopedforhome-landsecurityincludedtheReutechRadarSystem[18]andtheHARRIERGroundSurveillanceRadar(GSR)[19].

    ReutechRadarSystemdevelopedandlaunchedtheSpiderRSR940inJuly2009.ThefigureofSpiderRSR940 is shown inFigure16.1. It is ahighlymobile land-based,360continuous

    Figure 16.1 Spider RSR 940. (From http://www.rrs.co.za/products/homeland-security.html) AQ10

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    scanningsurveillance radar that iscapableofdetectingsurfaceandair targets. Italsoprovidessector scanningsurveillanceused fordetectionand identificationof slow-movingsurface-basedtargetssuchashumanbeings,smallboats,andevenhelicopters.Inadditiontothelocalcontrolstation,aremotecontrolandmonitoringcapabilityisalsoprovidedfortypicalapplicationinclud-ingthebordercontroloperations,monitoringofcoastaltraffic,coastlinecontrol,andmonitoringofprivateandunattendedairfield.Theradarsystemhasaninstrumentedrangeupto40km.Ontheotherhand,DeTectsradarprocessingtechnologydevelopedtheHARRIERGSRinyear2011,asshowninFigure16.2.HARRIERGSRusesstate-of-the-artsolid-stateDopplerradartechnol-ogyavailable inS-,X-,andcombinedS/X-bandradarfrequencies.Solid-stateradartechnologyappliedinHARRIERGSRofferssignificantincreasedperformance,longerusefullife,andlowermaintenancecostoverconventionalmagnetron-basedsystems.HARRIERGSRuseshigh-speedscanningforenhancedsmalltargetdetectioninhigh-clutterenvironmentssuchasdevelopedareas,terrain,andhighseastates.Italsohasautomaticdetectionandtrackingcapabilitiesandincludesuser-definedmonitoringandalarmzones.HARRIERGSRisofferedinfixedandmobileconfigu-rationsandcanbelinearlynetworkedtocoverlargeareassuchasbordercrossingsandcoastlines.

    Ingeneral,radar-basedhumanintruderdetectionsystemisexpensiveinhardware,sinceradarantennas(transmitterandreceiver),duplexer,waveguides,andelectronictoolsarerequiredtobesetup,anditalsorequiresspecificoperatingsoftware.Besides,thetargetmightbedetectedbytheradar,buttheirtypesorclassisnotknown.Itcanbesometimesvehicles,animals,ormovingobjects,whichgivefalsealarm.Also,somenarrowbandradarsmightsometimesfinddifficultiesinhavinginsufficientrangeresolutiontodiscriminatebetweenasmallernearbytargetandalargerlonger-rangetarget.

    16.2.3 Image Processing-Based Human Intruder Detection SystemThe third category of human intruder surveillance system is image processing-based humanintruderdetectionsystem.Itisbymeansofusingimagetotraceoutwhetherthereisanexistenceof trespasser/humanintruderornot.Imageprocessing-basedhumanintruderdetectionsystem

    Figure 16.2 HARRIER GSR. (From http://www.detect-inc.com/security.html)

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    iswidelyfavoredbymanypractitionersascomparedtoburglaralarmsystemsandradar-basedhumanintruderdetectionsystem,mainlyduetothesefourreasons:

    1.Ithelpscapturepictures.Theappliedsecuritycameraisagreattooltocaptureapictureoftheburglar/terroristwhentheyaretryingtotrespass/breakintoaprohibitedterritory.Thisisveryimportantbecausethesecuritycameragivestheauthoritiessomethingthattheycanusetohelpthemidentifytheburglar/terrorist.Havingasecuritysystemthattriggersanalarmisessential,butwithoutsecuritycameras,theauthoritieswillneverknowwhoorhowmanyburglars/terroriststriedtogetintotheirterritory.

    2.Morechanceoftheburglars/terroristsbeingcaught.Whentheauthoritiesareabletoviewthepicturesfromthecamerastoidentifytheburglars/terrorists,itprovideshigherpossibleratefortheburglars/terroriststogetcaught.Inmanyoccasionswhenauthoritiesarriveattheirincidentsite,theburglars/terroristswillbelonggone.Withoutthecapturedpictures,thereisnotmuchchancefortheburglars/terroristsbeingcaught.

    3.Securitycamerasaregreatpreventiontools.Theyaresomethingthatallburglars/terroristswilllookthroughbeforetheydecidetobreakinto/trespassaterritory.Mostoftheburglars/terroristswillnotevenattemptaterritoryiftheydetecttheexistenceofsecuritycamerasbecausetheyknowthatthisisgoingtoworkagainstthemandcausethemtogetcaught.Burglars/terrorists areknown for avoiding territory thathasgood security, especially theones that aremonitoredwith security cameras.Thecameraspose toobigof a threat forthem,sotheywillmoveontoatargetthatdoesnthavegoodsecurity.

    4.Securitycamerascansecurevulnerableareas.Whenauthorityisinsidetheinfrastructuresperimeterandneedstoseewhatishappeningoutsideofthenearbybuildingforsecurity,securitycamerasarethebestwaytoachievethatgoalsafely.Theauthoritycanwiselyplacesecuritycamerasineveryvulnerableareaofhisorherbuilding,indoorsandoutdoors,thatwouldbeaccessiblebyaburglar/terrorist.Thisallowstheauthoritytostaysafeinsidehisorherbuildingwithhisorherprotectedpersonwhilestillbeingabletoseewhatishappen-ingintheoutdoorareaofthebuilding.Italsogivesthemmoretimetoseekforhelpiftheynoticeanysecuritythreat.

    Ingeneral,theimageprocessing-basedtrespasserdetectionsystemcanbedividedintotwomaincategories:oneisvisionspectrumimageprocessing-basedhumanintruderdetectionsystemandanotheroneisnightvision/IRspectrumimageprocessing-basedhumanintruderdetectionsystem.

    16.2.3.1 Vision Spectrum Image Processing-Based Human Intruder Detection System

    Visionspectrumimageprocessing-basedhumanintruderdetectionsystemcanbedividedintotwocategories:oneisanalogvideosurveillancesystemandtheotheristhedigitalvideosurveil-lancesystem.

    16.2.3.1.1 Analog Video Surveillance System

    Datingbacktoasearlyas1965,analogvideosurveillancewasfirstbegunwithsimpleclosedcircuittelevision(CCTV)monitoring.TheU.S.pressreportssuggestingpolicealreadystartusingsurveil-lance cameras inmonitoringpublicplaces security. In1969,police installed sets of surveillancecamerasinNewYorkCityatthemunicipalbuildingnearthecityhall.Thispracticelaterspreadsto

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    othercitiesintheUnitedStateswithCCTVsystemsandkeepsaneyebypoliceofficersatalltimes.CCTVistheapplicationofvideocamerastosendimagesignaltoaspecificlocation,onalimitedsetofmonitors.ThefirstCCTVappliedinpublicplaceswascrude,conspicuous,lowdefinition,andinblackandwhitesystemsthatareunabletopanorzoomintoparticularview.Inmoderndays,CCTVsystemsapplysmaller-sizeandhigh-definitioncolorvideocamerasthatcanfocustoresolveminutedetailandalsocanlinkthecontrolofthevideocamerastoacomputer.Thismakestheobjectstobetrackedsemiautomatically.Thistechnology,so-calledvideocontentanalysis(VCA),iscurrentlyusedbyalargenumberoftechnologicalcompaniesaroundtheworldtoenablethesystemstorecognizewhetheramovingobjectisawalkingperson,acrawlingperson,ananimal,avehicle,etc.

    However, in themid-1990s, the emergingof digital technologyhas superseded the analogtechnologyinvideosurveillancesystem.Digitalmakesvideosurveillanceclearer,faster,andmoreefficient. Digital video surveillance has made complete sense as the price of digital recordingdroppedwiththecomputerrevolution.Insteadofchanginganalogvideotapesdaily,thedigitaluserscouldnowreliablyrecordamonthsworthofsurveillancecontentsonharddrivebecauseof itshighcompressioncapabilityand lower storagecost.Thedigitally recorded imagesare somuchclearerthantheanalog-recordedimages.Thisleadstotherecognitionprocessimmediatelyimprovingforpolice,privateinvestigators,andotherusersthatusevideosurveillanceforidenti-ficationpurposes.Byusingdigitaltechnology,theimagescouldalsobemanipulatedtofurtherimproveclaritybyaddinglight,enhancingtheimage,zoominginonframes,etc.

    16.2.3.1.2 Digital Video Surveillance System

    Digitaltechnologyisadatatechnologythatusesdiscretevalues[20].Digitalvideoisatypeofvideorecordingsystemapplyingdigitaltechnology.Thereisabroadrangeofdigitalvideosurveil-lancecamerasavailableinthemarket:

    Fake security cameras:thesecameraslooksimilartothosesurveillancecamerasavailableinthemarket,buttheyarenotactualcameras.Theyhavenorecordingcapability.Thesecam-erascanactasdeterrentcamerastoscareburglars/theft.Ifsomethinghappens,theywillnothavearecordsincetheyhavenorecodingcapability.

    Covert surveillance cameras: these cameras look like regular items, to hide its identityasasurveillancecamera, forexample,awallclockinashop,afacingfrontdoorteddybear,andapottedplantattheshopscorner.Eachoneofthemcouldveryeasilyembedasurveillancecamera.Thesurveillancecamerascanrecordthescenesanytimewithoutanybodyknowingitsexistence.

    Wireless security digital cameras:thesesurveillancecamerasareeasytoinstallandremoved,areoftensmallinsize,havenowiringconnectionseen,andoffermoreflexibilityinsetup.Thesecamerastransmitimagesignalswirelesslytoacenterhubthatareshownonamonitorscreeninamonitoringroom.

    Wired surveillance digital cameras: these surveillance cameras arewired and lackflexibilityinsetup.Theyareappropriateforpermanentsetup.Thesecamerastransmit imagesignalsthroughawiretoacenterhubthatareshownonamonitorscreeninamonitoringroom.

    Home surveillance cameras:thesecamerascomeinapackagethatoftenincludessomeextrafeaturessuchastimersforlamps,motionsensors,andautomaticgatedoorlock.

    Oneproblemencounteredinmostvisionspectrumimageprocessing-basedsurveillancesystemsisthechangeinambientlight,especiallyinanoutdoorenvironmentwherethelightingcondition

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    variesnaturally.Thismakestheconventionaldigitalcolorimageanalysistaskinsmartsurveil-lanceverydifficult.One commonapproach to alleviate thisproblem is to train the system tocompensateforanychangeintheillumination[2].However,thisisgenerallynotenoughforatrespasserinthedark.Itisbettertoapplysomesortofnightvisionimagingtoolsthatcanhelpimagingobjectsinthedark.

    16.2.3.2 Night Vision/Infrared Spectrum Image Processing-Based Trespasser Detection System

    Nightvisionistheabilitytoseeinadarkenvironment.Nightvisionismadepossiblebyacombi-nationoftwoapproaches:(1)sufficientspectralrangeand(2)sufficientintensityrange.Humanbeingshavepoornight vision ability compared tomany animalsbecausehumaneyes lack anelement,so-calledthetapetumlucidum.Thetapetumlucidum[2]isalayeroftissueintheeyeofmanyvertebrateanimals,whichliesimmediatelybehindorsometimeswithintheretina.Itreflectsvisible lightback through the retina, increasing the light available to thephotoreceptors.Thisimprovesvisioninlow-lightconditionsbutcancausetheperceivedimagetobeblurryfromtheinterferenceofthereflectedlight.Thetapetumlucidumcontributestothesuperiornightvisionofsomeanimals.Manyoftheseanimalsarenocturnal,especiallycarnivoresthathunttheorganismatnight,whileothersaredeepseaanimals.

    Thermalcameraisanexcellentnightvisionsecuritycamera.ItperceivesIRradiationanddoesnotneedasourceofillumination.Thermalcameraisidealforanylow-lightareas,notjustforthenighttime.Itproducesanimageinthedarkestofnightsandcanviewthroughlightfog,rain,andsmoke.Thermalimagingcamerasmakesmalltemperaturedifferencesvisible.Thermalimagingcamerasarecurrentlyappliedwidelyinmanyneworexistingsecuritynetworks.

    16.2.4 Directional versus Omnidirectional ViewingIn spite of the availability of many modern sophisticated surveillance monitoring products inthe market, majority of the systems have the limitation in the viewing angle of the camera.Omnidirectionalpromptstotheconceptoftheexistenceinalldirection,with360areacoverageonasingleplane/axis.Inimagingpointofview,anomnidirectionalvisualizationhasvisualizationcapabilityofa360fieldofviewaroundthehorizontalplaneorwithvisualfieldthatcoverstheentiresphere.Omnidirectionalvisualizationsystemisimportantinareasthatneedlargevisualfieldcoverage,suchasinpanoramicimagingandinrobotics.Aconventionalimagingtoolnor-mallyhasafieldofviewwiththerangeofafewdegreestomaximumof180.Itcancaptureonlyasemisphereimagewithlightfallingontotheimagingtoolsfocalpoint.However,ontheotherhand,anomnidirectional imagingtoolcancapture light fromalldirections (surrounded360fieldofview)fallingontoitsfocalpoint,coveringafullsphere.

    Convergentsecuritysystemsaresecuritysystemsthatintegrateintrusion,holdup,fire,videosurveillance,accesscontrol,andmonitoringapplicationsinphysicalsecuritysystemsandITinfra-structures. However, the current convergent security systems apply digital CCTV monitoringsystems, inwhichthecoverageareaisdirectional.Eventheycandoit inomnidirectional,butitrequiresmorehardware.Conventionalapproachestoobtainpanoramic(wideview)imageforanomnidirectionalviewmainlyconsistofcombiningsnapshotscapturedseparatelyintoasingleandcontinuousimage.Thiscombinationofimagesiscomputationallyintensivesometimes.AnexampleisbyusingaRANSACiterativealgorithm[21]tocombinethesnapshots.RANSACisanabbreviationforRANdomSampleConsensus.ThisalgorithmwasfirstpublishedbyFischler

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    andBollerin1981andfoundtobeusedinsolvingthecorrespondingproblem(partsofanimagecorrespondtopartsofanotherimage;aftertheimagingtoolhasmoved,timehaselapsedorthefocusingobjectsmovedaround)andcalculatesthefundamentalmatrixcorrespondingtoapairofstereoimagingtools.RANSACcanestimatetheparameterswithahighdegreeofaccuracybutwithlimitationthatthereisnoupperboundonthetimeittakestocomputetheseparameters.Toprocessanimage,itissometimestimeconsumingorendless.Evenifanuppertimeboundisused(setwithamaximumnumberofiterations),theresultsobtainedmaynotbetheoptimalone.Itmaynotbeonethatgeneratestheimageinagoodway.

    Besidescomputational intensive, thecombiningof images to formapanoramic imagealsodependsonthequalityandconsistencyofthesnapshotsused.Thesnapshotimagesmighthaveanumberofdeficienciesthatwillfurtherimpairthequalityoftheoutputpanoramicimage.Incomparison,anomnidirectionalimagingtoolcanbeusedtocreatereal-timepanoramicart,with-outpost-processingrequirement,andsomehowwillprovidemuchbetteroutputqualityimage.

    In robotics and computer vision, omnidirectional imaging tools are widely used in visualodometry [22] and also help solve the simultaneous localization and mapping (SLAM) [23]problemsvisually.Visualodometryistheprocessofdefiningthepositionandorientationofarobotbyanalyzingthecapturingimagesfromtheattachedimagingtools,whereasSLAMisatechniqueappliedbyautonomousvehiclesandmobilerobotstoformamapwithinanunknownenvironmentor toupdate amapwithin a known environment and in themeantimekeepon tracking theircurrentlocation.Duetotheomnidirectionalvisualizationsabilitytoobtaina360view,roboticandcomputervisiontaskscanhavebetterresultsforopticalflowandinfeatureselectionandmatching.

    Besidespanoramicart,robotics,andcomputervision,applicationofomnidirectionalvisual-izationalsoincludessurveillance,inwhichitisimportanttocoveralargevisualfield,andtele-conferencing,inwhichitisofgreatinteresttocoverasmanyparticipantsaspossibleinthesameimage,andyetthereareunlimitedapplicationsthatwillbediscoveredinthefuturesoon.Nextsectionwillfurtherdiscussthemethodsappliedtoacquireomnidirectionalviews.

    16.2.4.1 Optical Approach versus Mechanical Approach

    Omnidirectionalvisualizationpossessessignificantapplicationpotentialsintheareassuchaspan-oramicart,mobilerobotnavigationandcomputervision,qualitycontrol,andsurveillance.Themethodsappliedtoobtainomnidirectionalimagescanbeclassifiedintotwoapproaches[3]:

    1.Mechanical approach:themethodofgatheringimagestogenerateanomnidirectionalimage 2.Optical approach:themethodofcapturinganomnidirectionalimageatonce

    Inaddition,theyareclassifiedintotwocategoriesbytheviewpointoftheimage[3]:singleview-pointandmultipleviewpoints.

    For the mechanical approach, the images captured on a single viewpoint are continuous.Oneexampleistherotatingcamerasystem[2427].Insuchasystem,thecamerarotatesaroundthecenteroftheprojection.Itgeneratesanomnidirectionalimagefromasingleviewpoint.Theproperorderofimagesobtainedbyrotationis joinedtogethertoacquireapanoramicviewforthescene.AnexampleofrotatingcameraisshowninFigure16.3.Arotatingmotorisrequiredtorotatethevideocamerainordertoscantheomnidirectionalview.However,sinceitisnecessarytorotateavideocamerainafullcircleinordertoacquireasingleomnidirectionalimage,itisimpossibletogeneratereal-timeomnidirectionalimage.Otherdisadvantagesofrotatingcamerasystemarethatitrequirestheuseofmovingpartsandprecisepositioning.Theimagecaptured

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    atmultipleviewpointsisrelativelyeasytoconstruct,asshowninFigure16.4.Asinglecameraormorecamerasareappliedtogathermultipleimagesatmultipleviewpointsandcombinethemintoanomnidirectionalimage.QuickTimeVRsystem[28]adoptedsuchtechnologiesandhasmanymarketapplications.However,theimagesgeneratedbythesystemarenotalwayscontinuousandconsistent,anditalsocannotcapturethedynamicsceneatvideorate.

    Sincemechanicalapproachleadstomanyproblemsondiscontinuityandinconsistency,opti-calapproachwasinuse.Thisapproachismostappropriateforreal-timeapplicationsanditisofsingleviewpoint.Thistypeofapproachneednotusemotorsandinthemeantimeitcancaptureomnidirectionalimageatonce,noextracombinationworkrequired,anditisfast.Twoalterna-tiveshavebeenproposed,namely,theuseofspecial-purposelens(suchasthefish-eyelens[29])andtheuseofhyperbolicopticalmirrors[30].Fish-eyelens,asshowninFigure16.5,areusedtoreplaceaconventionalcameralensthathaveveryshortfocallengththatallowsthecameratoviewobjectsasmuchasinahemispherescene.Fish-eyelenshavebeenwidelyusedforwide-angleimagingareasasnotedinRefs.[31,32].However,NalwasworksinRef.[33]foundoutthatitisdifficulttodesignfish-eyelensthatensurethatall incomingprincipalraysintersectatasinglepointtoyieldafixedviewpoint.Theacquiredimageusingfish-eyelensnormallydoesnotpermittheconstructionofdistortion-freeperspectiveimagesoftheviewedscene.Hence,tocaptureanomnidirectionalview,thedesignofoptimalfish-eyelensmustbequitecomplexandlarge,andtherefore,theyareexpensiveincost.Besides,accordingtoRef.[34],therelativeilluminationforafish-eyelensdesigniswidelyvarying.Inaddition,theexistencesofdistortionacrossthehemi-sphericalfieldofviewneedtobeinconsiderationwhendesigningagoodqualityfish-eyelens.Sincefish-eye lensareexpensiveandcomplexindesignandalmostprovidethesamereflectivequalityashyperbolicopticalmirror,hyperbolicopticalapproachisplannedtoadapt.

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    Figure 16.3 Rotating camera. (From Stun-ningsales.com, Weatherproof CCD color rotating video security camera, redirecting from http://www.stun-ningsales.com/homethings/outdoor_securitycameras.htm)

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    Figure 16.5 Fish-eye lens. (From The-Digital Image.com, Fisheye lens, redirecting from http://www.the-digital-picture.com/Reviews/)

    Figure 16.4 Multiple cameras system. (From Chen, S.E., Quick time VR: An image-based approach to virtual environment navigation, in Proceedings of the 22nd Annual ACM Conference on Computer Graphics, Los Angeles, CA, pp. 2938, 1995.)

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    16.2.4.2 Vision Spectrum-Based Omnidirectional Surveillance System

    The proposed vision spectrum-based omnidirectional surveillance system model is shown inFigure16.6.

    Inthismodel,omnidirectional imagesofanobservedscenearecapturedusing thecombi-nation of a web camera (webcam) and a specific design hyperbolic optical mirror. MATLABinthe laptopcomputerwillperformunwarpingonthe imagescaptured intopanoramic form.Thehuman intruderdetection algorithm that is programmed inMATLABwill thenbeusedtoprocess thepanoramic images todetect thepresenceofhumanintruder. Ifhumanintruderisdetected,alarmwillbesignaledandportionsofsuspectedimagewithhumanintruderwillbestoredinadatabaseforfurtheridentificationpurposes.

    ThesurveillancecamerasetusedinthissurveillancesystemconsistsofawebcameraandanattachedspecificdesignhyperbolicopticalmirrorasshowninFigure16.7.

    Laptopcomputer

    Alarm

    Webcamspecificdesign

    hyperbolicmirror

    +

    Figure 16.6 Omnidirectional surveillance system model.

    Figure 16.7 Surveillance camera set (web camera and specific design hyperbolic optical mirror).

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    ThewebcamerausedinthesurveillancesystemisE3500PlusQuickCambyLogitech.Itisadomesticwebcamerathatcaptureshigh-qualityVGA(640480)videosand1.3megapixel(soft-ware-enhanced)images.ThewebcameraissmallinsizeandcheapcomparedtodigitalandCCTVcamerawithfine-resolutionoutput.ThedigitalcontrolisalsoaccomplishedthroughtheUSBportconnectedtoalaptoporpersonalcomputer(PC)viaplugandplayonWindowsXPorVista.ItcanbeinterfacedwithMATLABtoo.Therefore,itisbestoutfittedinomnidirectionalsurveillancesystem.

    Thespecificdesignhyperbolicopticalmirrorusedintheomnidirectionalsurveillancesystemisasmall-sizewide-viewtype,withouterdiameterof40mmandangleofview30abovehorizon-talplanemanufacturedbyACCOWLEVISION.Themirrorcanreflecta360viewsurroundedbyitself,andasthewebcameraplugsonit,omnidirectionalimageswithinaguardedperimetercanbecapturedandsenttoalaptopcomputerinamonitoringroomtobeprocessedforsurveil-lancepurpose.A custom-madebracket that is shown inFigure16.8 is designed to attach thehyperbolicmirrortothewebcameraviaasocket.

    Alaptopcomputercanbeusedforimageprocessingofanobservedroom.Acore2duolaptopcomputerwithspecs1.83GHzprocessorand2GBDDR2RAMwithMATLABver.7.0ischo-sentobeusedhere.MATLABhasadataacquisitiontoolboxinterfacethatenablesattainmentofvideosandimagesthroughtheE3500PlusQuickCam.Asetofconnectingspeakerstothelaptopcomputersoundsthealarmifhumanintruderisdetected.

    16.2.4.3 Thermal/Infrared Spectrum-Based Omnidirectional Surveillance System

    Oneproblemencounteredinmostsurveillancesystemsisthechangeinambientlight,especiallyinoutdoorenvironmentwherethelightingconditionisnaturallyvarying.Thismakesthevideoanalysistaskinsmartsurveillanceverydifficult.Onecommonapproachtoalleviatethisproblemistotrainthesystemtocompensateforanychangeintheillumination.However,thisisgenerallynot enough forobject trackingandmonitoring in thedark. In recent times, severalmanufac-turershavecomeupwithhighlysophisticatedthermalcameraforimagingobjectsinthedark.ThecamerausesIRsensorsthatcaptureIRcomingfromdifferentobjectsinthesurroundingand

    Figure 16.8 Front view of the custom-made bracket.

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    formanIRimage.SinceIRradiationfromanobjectisduetothethermalradiation,theimageformationwilldependontheobjecttemperatureandnotonthelightreflectedfromtheobject.Hence,suchcameracanbeconvenientlyusedfornightvision.Thepresentstateoftheartevenallowsthermalcameratocaptureobjectevenfromaverylongdistance.

    Ifasinglethermalcameraistomonitorthesecurityofasinglelocation,thenformoreloca-tions indifferentanglesofview,more thermalcamerasarerequired.Hence, itwillcostmore,besidescomplicatingthesurveillancenetwork.Theproposedthermal/IRspectrum-basedomnidi-rectionalsurveillancesystemmodelisshowninFigure16.9.Thissystemrequiresacustom-madeIR-reflectedhyperbolicmirror,acameramirrorholder,afine-resolutionthermalcameraandalaptoporPC installedwithMATLABprogramming (versionR2007bor later), andanalarmsignalingsystem.Thealarmsignalingsystemcanbeassimpleasacomputersspeaker.

    Thebestshapeofpracticaluseomnidirectionalmirrorishyperbolic.AsderivedbyChahlandSrinivasaninRef.[37],allthepolynomialmirrorshapes(conical,spherical,parabolic,etc.)donotprovideacentralperspectiveprojection,exceptforthehyperbolicone.Theyalsoshowthatthehyperbolicmirrorguaranteesalinearmappingbetweentheangleofelevationandtheradialdistancefromthecenteroftheimageplane.Anotheradvantageofhyperbolicmirroriswhenusingitwithacamera/imagerofhomogenouspixeldensity,theresolutionintheomnidirectionalimagecapturedisalsoincreasingwithgrowingeccentricity,andhence,itwillguaranteeauniformresolutionforthepanoramicimageafterunwarping.

    TheresearchgroupofOMNIVIEWSproject fromCzechTechnicalUniversityfurtherdevel-opedMATLABsoftwarefordesigningomnidirectionalmirror[38].FromtheMATLABsoftware,omnidirectionalhyperbolicmirrorcanbedesignedbyinputtingsomeparametersthatspecifythemirrordimension.Thefirstparameteristhefocallengthofthecameraf,inwhichforthethermalcamerainuseis12.5mmandthedistanced(zplane)fromtheoriginissetto2m.Theimageplaneheighthissetto20cm.Theradiusofthemirrorrimischosent1=3.6cmasmodifiedfromSvobodasworkinRef.[39],withradiusforfovearegion0.6cmandretinaregion3.0cm.Foveaangleissetbetween0and45,whereasretinaangleisfrom45to135.ThecoordinatesaswellastheplotofthemirrorshapearegeneratedusingMATLABandshowninFigure16.10.ThecoordinatesaswellasmechanicaldrawingusingAutoCADareprovidedtoprecisionengineeringcompanytofabricate/custommadethehyperbolicmirror.Thehyperbolicmirrorismilledfromaluminumbarandthenchromed.Chromiumisofgreatinterestbecauseofitslustrous(goodinIRreflection)property,highcorrosionresistance,highmeltingpoint,andhardness.ThefabricatedmirrorisshowninFigure16.11.

    Thecameramirrorholderisself-designedandcustommadewithaluminummaterialasshowninFigure16.12.Thethermalcamerausedisanaffordableandaccuratetemperaturemeasurementmode:ThermoVisionA-20MismanufacturedbyFLIRSystems.Thethermalcamerahasatemper-aturesensitivityof0.10rangingfrom20Cto350C.However,forhumandetection,thetemper-aturerangeissettorangefrom30Cto40C.Thethermalcameracancapturethermalimageswithfineresolutionupto320240pixelsofferingmorethan76,000individualmeasurementpoints

    Custom made IRreflected hyperbolicmirror + cameramirror holder set

    Thermalcamera

    Capturethermalimage

    Laptop/PC

    Processimage and

    signal alarm

    Figure 16.9 Omnidirectional thermal imaging surveillance system model.

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    perimageatarefreshrateof50/60Hz.TheA-20Mfeaturesachoiceofconnectivityoptions.Forfastimageanddatatransferofreal-timefullyradiometric16bitimages,anIEEE-1394FireWiredigitaloutputcanbechosen.Fornetworkand/ormultiplecamerainstallations,Ethernetconnec-tivityisalsoavailable.EachA-20McanbeequippedwithitsownuniqueURLallowingittobeaddressedindependentlyviaitsEthernetconnection,anditcanbelinkedtogetherwitharoutertoformanetwork.Therefore,itisbestoutfittedforhumanintruderdetection.

    Figure 16.11 Fabricated mirror.

    3

    2.5

    2

    1.5

    1

    0.5

    0 3 2 1 0 1 2 3

    cm

    cm

    Figure 16.10 Mirror coordinates plot in MATLAB.

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    Aproblemencountered in thermalcamera selection is theexistenceof thehaloingeffect inuncalibratedferroelectricbariumstrontiumtitanate(BST)sensors.Haloingeffectisthepresenceofhalosaroundobjectshavingahighthermalcontrastwiththebackground[40].A-20Mischo-senbecauseitusestheuncooledmicrobolometerFPAdetectortechnologythatdoesnotproducethehaloingeffect.A laptoporPCcanbeusedas imageprocessor,placedeitheronsiteor inamonitoringroom.MATLABversionR2007bprogramming ischosentobeusedbecause ithasuser-friendlysoftwareforperforminglog-polarmappingtechniquetounwraptheomnidirectionalthermal image into panoramic form and it can partition the panoramic thermal images easilyaccordingtoeachsinglelocationtobemonitoredandprocessthemsmoothlywiththetrespasserorfaintdetectionalgorithmuserprogrammedin.Thealarmwillbetriggeredonceahumanbeingisdetectedinatestedimageforhumanintruderdetectionmode.TheoverallfabricatedsystemmodelisshowninFigure16.12.

    16.3 Unwarping MethodsThecapturedomnidirectionalimageshavedifferentpropertiescomparedtoperspectiveimagesintermsofimagingdeformation.Suchdistortionleadstotheimagesbeingdifficulttobedirectlyimple-mented.Thus,itisnecessarytoworkoutanefficientmethodtounwarptheomni-image.Unwarping,generally,isamethodusedindigitalimageprocessinginopeningupanomnidirectionalimageintoapanoramicimage,makingtheinformationontheimagebeabletobedirectlyimplementedand

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    Figure 16.12 Overall fabricated omnidirectional thermal imaging surveillance system model.

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    understood.Thissubsectionstudiesthreeuniversalunwarpingmethodsthatarecurrentlyappliedactivelyaroundtheworldintransformingomnidirectionalimagetopanoramicimage,namely,theDGTmethod[4],thepano-mappingtablemethod[5],andthelog-polarmappingmethod[6].

    16.3.1 Discrete Geometry Technique MethodDGTmethod,bythenameitself,meansthatthistechniqueisusedbyapplyingonebyonethegeometryoftheimage,discretely,inordertosuccessfullyunwarptheomnidirectionalimageintoapanoramicimage.ThismethodispracticallyusedintransformingtheomnidirectionalimagesintopanoramicimagesonacylindricalsurfaceusingPDE-basedresamplingmodels[4].

    InDGTmethod,itisrequiredtoperformthecalculationofeachandeverypixelintheomni-imagefirstandthencheckforitscorrespondingradiusfromthecenteroftheomnidirectionalimageandlaterdeterminewhetheritshouldbeconsideredornot.Thecalculationsstartfromafixedposi-tionanddirection,suchasfromtherightgoingcounterclockwisefor360.Foraradiusof1,acircleofradius1willbevisualizedinthecenterofomnidirectionalimage,whichinotherwordsmeansthatthecirclewillbeinsizeof33pixels.Allthepixelsinthisboundaryof33pixelswillbeconsidered,andtheircorrespondingradiuswillbecalculated.Allpixelsthatfallwithintheradiusof1,whichistheradiusofconcern,willbeconsideredintheconversion.Duetothepixelsthataregenerallyanareaofdatainformation,itispossiblethatthecirclewilllieinbetweenthepixels.Therefore,atoleranceofradiusissettocounterthisproblem.Inotherwords,acircleofradius1willconsiderthepixelslyingbetweenradiusof0.5and1.4,andacircleofradius2willconsiderthepixelslyingbetweenradiusof1.5and2.4,andsoon.AnexampleisshowninFigure16.13.

    Assoonasapixel intheboundaryisdeemedtobeconsideredorinrangeoftheradius, itwillbemappedintoanewmatrixofpanoramicimage.However,sincethepixelsmappedintothepanoramicimagemustbeinordersothattheimagewillnotbedistorted,theimagewillbesplitintofoursectionsof90each,asshowninFigure16.14,whereeachsectionwillperformthecalculationbasedonthemovingdirectionofthecircle.Forexample,foracircledrawn,startingfromtherightinacounterclockwisedirection,thepixelsinthesectionattheupperrightpartwillbetakenandcalculatedonebyone,fromthebottompartofthesectionandfromrighttoleft,whichwillthenbeincreasedonebyone,tilltheupperpartofthesection,fromrighttoleftaswell.Ontheotherhand,forthelowerleftpartofthesection,thecalculationwillgofromthetopofthesection,goingfromlefttoright.

    However,duetothepixelsbeingconsideredfordifferentcirclesofdifferentradiithatwillbenonuniform,asshowninFigure16.15,aresamplingprocessisneededtostandardizethepixelsin

    Pixels lyingin between

    Figure 16.13 Circle lying in between pixels.

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    everyrowofthepanoramicimage.Therefore,aftereverypixelinthewholeomni-imageismappedontothepanoramicimageplane,spacingwillbeinsertedinbetweenpixelsineveryrow(asshowninFigure16.16)inordertostandardizetheresolutionofthepanoramicimageforeachrow.Thiswillgenerateastandardresolutionofpanoramicimage.However,duetospacingthatisgenerallyemptypixelswithnodatainformation,arowwithverylittlepixelswillbehardlyunderstandable.Therefore,thepixelswillbeduplicatedoverthespacing,insteadofinsertingemptypixelsintoit,andanunderstandableuniformresolutionpanoramicimagecanbegenerated.

    16.3.2 Pano-Mapping Table MethodThis method uses a table, which is so-called the pano-mapping table, to process the imageconversion.Pano-mappingtablewillbecreatedonceandforall,consistingofmanycoordi-natescorresponding to thecoordinates taken fromtheomnidirectional image thatwill thenbemappedintoanewpanoramicimage,respectively.Itispracticallyusedinomnidirectionalvisual tracking[41]andtheunwarpingprocessofomni-images takenbyalmostanykindofomni-camerasprior to requiringanyknowledgeabout thecameraparameters inadvance, asproposedbyJengetal.[5,8].

    Figure 16.14 Circle being split into four sections.

    Figure 16.15 Nonuniform resolution of panoramic image.

    Figure 16.16 Spacing is inserted in between pixels, denoted by black dots.

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  • 434 Effective Surveillance for Homeland Security

    Inpano-mappingtablemethod,itisrequiredtoselectfivelandmarkpointsfromtheomni-directionalimagefirst.Thesepointswillbetakenfromthesameline,drawingfromthecenteroftheomni-imagetothecircumferenceoftheimage,whichinotherwordsiscalledtheradiusoftheimage.Fivepointsinbetweentheendofthislinewillbepicked,andthevaluecorrespondingtotheirradiusfromthecenterisobtained.Itisthenusedinordertoobtainthefivecoefficientsofa0througha4intheradialstretchingfunction,fr(),describedbythefollowing4th-degreepolynomialfunctionof

    r f a a a a ar 1 1 2 2 3 3 4 4= ( ) = + + + + 0 (16.1)

    wherercorrespondstotheradiusistheparticularradiusfortheeachofthefivepointstakena0a4arefivecoefficientstobeestimatedusingthevaluesobtainedfromthelandmarkpoints

    Oncethefivecoefficientsareobtained,thepano-mappingtable,TMN,canthenbegenerated.Thesizeofthetablewillfirstbedeterminedmanually,bysettingittoatableofsizeMN.Hence,inordertofillupatablewithMNentries,thelandmarkpoint,whichcorrespondstotheradiusof theomnidirectional image,willbedivided intoMseparatedparts, and theanglewillbedividedintoNpartsasfollows:

    ij

    i radiusM

    = (16.2)

    ij

    j 36

    N= 0 (16.3)

    andthecalculationwillbeprocessed,bytakingthefirstpointwherei=1andj=1,whichgives11=radius/Mand11=360/N.Thevalueofijwillthenbesubstitutedintotheradialstretchingfunctioninordertoobtaintheparticularradiusatthatparticularlandmarkpoint.Thisradiusobtainedwillthenbesubstitutedintotheequationasfollowstoberoundedup,inordertogetthecorrespondingcoordinatesintheomnidirectionalimage:

    v r cos= (16.4)

    u r sin= (16.5)

    wherevanducorrespondtothex-andy-coordinatesoftheomnidirectionalimage.Thiscoor-dinate(u,v)obtainedisinsertedintothepano-mappingtableTMN=Tij.TheuandvwillthenbeprocessedforNtimesbyincreasingjforNtimestoobtaindifferentangles,,tolaterdetermineall the coordinates corresponding to the value of landmarkpoint.These coordinates obtainedareinsertedintothetableofi=1withtheircorrespondingj=1toj=N,andtheiwillthenbeincreasedby1,andtheprocessisrepeatedforj=1toj=Ntodetermineallcoordinatesrelatedtoi=2.ThisiwillberepeatedforMtimes,andatableofMNentrieswithallthecoordinatescanbegenerated.Thecoordinatesineachoftheentriesaretakenonebyone,inordertomapeachandeverypixelintheomnidirectionalimagewiththecoordinateinthecurrententry,intoanewpanoramicimage.Theconversioniscompletedupontheendofmappingofthetable.

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    16.3.3 Log-Polar Mapping MethodLog-polarmappingisatypeofspatiallyvariantimagerepresentationwherebypixelseparationsincreaselinearlywithdistance.ItenablestheconcentrationofcomputationalresourceonanROIaswellasmaintainingtheinformationfromawiderview.Thismethodisimplementedbyapply-inglog-polargeometryrepresentations.ThecapturedomnidirectionalimagewillfirstbesampledbyspatiallyvariantgridfromaCartesianformintoalog-polarform.Thespatiallyvariantgridrepresenting log-polarmappingwill thenbe formedby inumberof concentric circleswithNnumberofsamples,andtheomnidirectionalimagewillthenbeunwarpedintoapanoramicimageinanotherCartesianform.

    Thismethodispracticallyusedinrobustimageregistration[43],orinroboticvision,particu-larlyinvisualattention,targettracking,egomotionestimation,and3Dperception[44],aswellasinvision-basednavigation,environmentalrepresentations,andimaginggeometries[45],byJosSantos-VictorandAlexandreBernardino.Inlog-polarmappingmethod,thecenterpixelforlog-polarsamplingiscalculatedby

    x y x x y yi i i c i c,( ) = ( ) + ( )2 2 (16.6)

    x y

    N y yx x

    i ii c

    i c

    , tan( ) =

    21 (16.7)

    andthecenterpixelforlog-polarmappingiscalculatedby

    x xo c , cos( ) = + (16.8)

    y yo c , sin( ) = + (16.9)

    wherexc,ycarethecenterpointsofouroriginalCartesianformcoordinateNisthenumberofsamplesineachandeveryconcentriccircletaken

    Theoriginal(xi,yi)inCartesianformissampledintolog-polarcoordinateof(,),asshowninFigure16.17.Thecenterpointiscalculatedbyusing(16.6)and(16.7)togettherespectiveand,whichcoveraregionoftheoriginalCartesianpixelsofradius:

    r brn n= 1 (16.10)

    and

    b

    NN

    = +

    (16.11)

    whereristhesamplingcircleradiusbistheratiobetweentwoapparentsamplingcircles

    Figure16.18showsthecircularsamplingstructureandtheunwarpingprocessdonebyusingthelog-polarmappingmethod[43].Themeanvalueofpixelswithineachandeverycircularsampling

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  • 436 Effective Surveillance for Homeland Security

    iscalculatedandwillbeassignedtothecenterpointofthecircularsampling.Theprocesswillthencontinuebymappingthemeanvalueatlog-polarpixel(,)intoanotherCartesianformusingEquations16.8and16.9,andtheunwarpingisdoneattheendofmapping.

    16.3.4 Performance EvaluationThis subsection reports the performance evaluation for different unwarping methods. Fewimportantfactorsareselectedfortheperformanceevaluationoftheunwarpingmethods.Thesefactorsincluderesolutionoftheimagegenerated,qualityofimage,algorithmusedinperform-

    A

    Center ofimage

    AB

    BB B

    AA

    y2

    x2

    Figure 16.18 Circular sampling structure and the unwarping process.

    Capturedimage

    y1

    x1Sampling Mapping

    x2

    y2

    N

    (0,0)

    Figure 16.17 Process of log-polar mapping.

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    ingtheunwarpingprocess,complexity,processingtime,anddatacompression.SomecapturedomnidirectionalimagesasshowninFigure16.19willbeusedtotesttheunwarpingmethods.

    1.Resolution of the image generated:Theresolutionofeachgeneratedpanoramicimageusinglog-polarmappingmethod,DGTs, andpano-mapping tablemethod isdiscussed in thissubsection.Thelog-polarmappingmethodprovidessmallerresolutionofdimensionthatequalsto1/4-foldoftheomnidirectionalimage,whereasfortheDGTmethodandpano-mapping tablemethod, the resolutionof thepanoramic imageproducedcanbe as largeasthelengthoftheperimeteroftheomnidirectionalimage,withthewidthequalstotheradiusoftheomnidirectionalimage.However,duetotheimagesbeingrescaledforviewingpurposes,thedifferenceisnotobviousinthischapter.

    2.Quality of image: Since the images are rescaled, thedifference inquality isnot apparentaswell.However,pano-mappingtablemethodisfoundtoproducethehighestqualityofimage,followedbythelog-polarmappingmethod,andtheDGTmethodcorrespondinglyindescendentqualityorder.

    (a-1) (a-2)

    (b-2)

    (c-2)

    (d-2)

    (b-1)

    (c-1)

    (d-1)

    Figure 16.19 Performance evaluation (a-1, a-2). Samples of omnidirectional images (b-1, b-2). Panoramic images generated using DGT method (c-1, c-2). Panoramic images generated using pano-mapping table method (d-1, d-2). Panoramic images generated using log-polar method.

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    3.Algorithm used in performing the unwarping process: In log-polar mapping algorithm, theomnidirectionalimageisconsideredintheformofanumberofsectorsinwhicheachsectorconsistsofagroupofpixelsthatwillbeextractedlaterinsectorbysectortobearrangedintoarectangularformofimage,whereasfortheDGTmethod,pixelbypixelistobeextractedandarrangedintoarectangularformimage.Thesepixelswillthenbereproduced,ordupli-cated,inordertostandardizethenumberofpixelsavailableineachrowofthepanoramicimage.Forthepano-mappingtablemethod,analgorithmisusedwherebyatableiscreatedatinitialization,toindicatethecoordinatesofthepixelstobeextractedfromtheomnidirec-tionalimage.Oncethetableiscreated,itwillthenbeusedoverandoveragaintomapeachofthepixelatthatparticularcoordinate,onebyone,fromtheomnidirectionalimageintoapanoramicimage,hencethenameonceandforall.

    4.Complexity:Table16.1showsthebig-Ocomplexityof log-polarmappingmethod,DGTmethod,andpano-mappingtablemethod.

    5.Processing time:Theprocessingtimeforallthethreeunwarpingmethodstotransformanomnidirectional image into a panoramic image is calculated using MATLAB functioncputime.Theprogram isprocessedfive timesonfivedifferent images, and theaverageprocessingtimeiscomputed.Itisfoundthatpano-mappingtablemethodhasthefastestcomputationtime,whichis1.220s,followedbylog-polarmappingmethodbeing2.003sand3.426sfortheDGTmethod.

    6.Data compression:Thegeneratedpanoramicimageproducedbylog-polarmappingmethodhastheresolutionof473114;DGTmethodhasaresolutionof1472235and1146243forpano-mappingtablemethod,inwhichtheoriginalomnidirectionalimageisofresolu-tion473473.Fromtheoutputresolution,itisclearthatlog-polarmappinghasthehighestcompression,whichcompressestheimageuptofourfold,comparedtoDGTmethod(0.65-foldimageexpansion)andpano-mappingtablemethod(0.80-foldimageexpansion).

    Intermsofresolution of the image generated,althoughtheimagegeneratedbyDGTmethodandpano-mappingtablemethodsislargerascomparedtotheimagegeneratedbylog-polarmappingmethod, these twomethods seemtoelongate theactual sizeof the image. Inotherwords, this

    Table 16.1 Big-O Complexity

    DGT Log-Polar Mapping

    Pano-Mapping Table

    AdditionO(XY2) O(X2Y2) O(Y2)

    Subtraction

    MultiplicationO(Y) O(X2) O(Y2)

    Division

    Logarithmic O

    log XY

    ( )( )

    log

    X, length of the panoramic image = perimeter of the omni-directional image taken into consideration Y = height of the panoramic image = radius of the omnidirectional image taken into consideration.

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    methodtendstomaketheobjectsintheimageextendedandbroaderthantheoriginalimage.Duetothiselongation,itwillbehardertoexaminethepictureandtheobjects,asthesenseofthesizehadbeeneliminated.Forlog-polarmappingmethod,theextensionisnotmuch,anditisnotasobviousasDGTmethodandpano-mappingtablemethods.Intermsofquality of image,pano-mappingtablemethodproducesthehighestqualityamongthethreemethods,followedbylog-polarmappingmethodwithaslightlylowerimagequalitybutstillwithinanacceptablerange,andlastlytheblurredDGTmethod.Intermsofalgorithm used in performing the unwarping process,pano-mappingtablemethodusesthesimplestandeasiestalgorithm,followedbyaslightlycomplexalgorithmthatisthelog-polarmethod,andlastly,acomplicatedandcomplexalgorithmfromtheDGTmethod.Intermsofcomplexity,itisfoundthatpano-mappingtablemethodhastheleastcomplexity,followedbyDGTmethod,andlastlylog-polarmappingmethodinbig-Onotation.Intermsofprocessing time,onaverage,pano-mappingtablemethodhasthefastestprocessingtimetotransformanomnidirectionalimageintoapanoramicimage,followedbylog-polarmappingmethodandDGTmethod.Intermsofdata compression,log-polarmappingmethodhasthebestdatacompressionratecomparedtopano-mappingtablemethodandDGTmethod.ThisisverygoodinpreservingCPUsmemory,asthememoryavailableisusuallyverylimited.

    16.4 Automatic Human Intruder Detection AlgorithmAutomatichumanintruderdetectionis implementedintheproposedomnidirectional imagingsystemtoanalyzeinformationfromthepositionoftheimagingtoolsandautomaticallydetectatrespasser.Twoautomatichumanintruderdetectionalgorithmsarediscussedinthissubsection;thisincludespartitionedROIalgorithm[11]andhumanheadcurvetestalgorithm[12,13].

    16.4.1 Partitioned Region of Interest AlgorithmThepartitionedROI-basedhumanintruderdetectionalgorithmissummarizedasfollows:

    Step1:Adjustthethermalcameradetectionrangingfrom30Cto40Csothatobjectwithhumanbodytemperaturerangecanbedetected.

    Step2:Unwarptheomnidirectionalthermalimageintopanoramicthermalimageusinglog-polarmappingtechnique.

    Step3:CaptureimagescontinuouslyfromthermalcameraintolaptopandnameitasPxwherex=1,2,3,isthediscrete-timeinstant.

    Step4:Divideeach imagecaptured fromthermalcamera into (mn) regions.Each regionconsistsofequalnumberofpixels.

    Step5:DefineamatrixMwithsizeof(mn)torepresentthecharacteristicofeachcorrespond-ingregion.

    Step6:DefineathresholdvalueQ.QisthethresholdvalueofthedifferencebetweensumsofRGBvalueforaparticularcurrentimagepixelandpreviousimagepixel.

    Step7:DefineavariablehforcountingthenumberofpixelsexceedingQ.Initially,h issetto0.

    Step8:DefineHasaminimumnumberofpixelswithdifferenceexceedingQ.Step9:ComparecurrenttakenimagePxwithprevioustakenimagePx1.Foreachcorrespond-

    ingregion,findoutthedifferencebetweenaparticularcurrentimageandpreviousimagepixelssumofRGBvalues.IfthedifferencebetweensumsofRGBvaluesforaparticular

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    currentimagepixelandpreviousimagepixelQ,thenh=h+1.IfhH,marka1intothecorrespondingelementofM;elseifh2,thentheobjectisnotconsideredasahumanbeingandthenextsubsequentstepsinthisalgorithmcanbeskippedandproceedwiththenextobject.Else,iftheobjectispossiblyahumanbeing,continuestep8forfurtherdetection.

    Step8:Neckbodyposition test:Calculatex,which is thedistancebetweenxcandxmwherexc=horizontalcenterbetweenPldandPrdandxmisobtainedinstep1.Definewn=horizontaldistancebetweenPldandPrd.

    Ifx2wn,thenthisobjectisnotclassifiedasahumanbeingandthenextsubsequentstepsinthisalgorithmcanbeskippedandproceedwiththenextobject.Elseifx