411 Chapter 16 Omnidirectional Human Intrusion Detection System Using Computer Vision Techniques Wai Kit Wong, Chu Kiong Loo, and Way Soong Lim Contents 16.1 Introduction .................................................................................................................... 412 16.2 Human Intruder Surveillance System ............................................................................. 414 16.2.1 Burglar Alarm System.......................................................................................... 414 16.2.1.1 Passive Infrared Motion Detector System .............................................. 415 16.2.1.2 Ultrasonic Motion Detector System ...................................................... 415 16.2.1.3 Glass-Break Detector System ................................................................. 416 16.2.1.4 Photoelectric Beam Systems................................................................... 417 16.2.1.5 Vibration Sensor System ........................................................................ 417 16.2.1.6 Passive Magnetic Field Detection System............................................... 417 16.2.1.7 Microphonic Detection System.............................................................. 418 16.2.1.8 Taut Wire Perimeter Security System..................................................... 418 16.2.2 Radar-Based Human Intruder Detection System................................................. 419 16.2.3 Image Processing-Based Human Intruder Detection System .............................. 420 16.2.3.1 Vision Spectrum Image Processing-Based Human Intruder Detection System .......................................................421 16.2.3.2 Night Vision/Infrared Spectrum Image Processing-Based Trespasser Detection System.................................................................................. 423 K13920_C016.indd 411 1/4/2013 7:24:10 PM
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Omnidirectional Human Intrusion Detection … Human Intrusion Detection System 415 busses, etc.) and their contents. Burglar alarm systems (or intrusion detection systems, perimeter
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411
Chapter 16
Omnidirectional Human Intrusion Detection System Using Computer Vision Techniques
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16.1 IntroductionHomelandsecurityisaneffortbygovernment(normallyparkedundernationaldefensedepart-ment)topreventterroristattack inacountryandreduceacountry’svulnerabilitytoterrorism[1].Thescopesofhomelandsecurityonhumantrespasserdoincludetheprotectionofacriticalinfrastructure’sperimeterandthebordersecurity(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,ontrespassers’threats.Thischapterproposessomehomelandsecuritysystemsonhumanintruderdetection.
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,isamethodusedindigitalimageprocessingin“opening”up 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].Itisafactthatahuman’svisualattentiondropsbelowacceptablelevelswhenassignedtovisualmonitoring,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.Ahumanintrudertrespassestoacriticalinfrastructure’sperimeterandthebordersecurity 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.
Humanintrudersurveillancesystemcanbeusedtohelpsecurityauthorityguardacriticalinfrastructure’sperimeterandthebordersecurity.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.
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.
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.
Microphonic-baseddetectionsystemshaveavarietyofdesign,butallaregenerallybasedonthedetectionof a trespasser attempting tocutor climbover a chainwire fence.Themicrophonicdetectionsystemsareusuallyinstalledassensorcablesattachedtorigidchainwirefences.Oneexampleisthemicrophonicfencedisturbancesensorsystem.Microphonicfencedisturbancesen-sorsapplythesignalsgeneratedbytheminuteflexingoftriboelectriccoaxialsensorcable,whichareanalyzedbypowerfulsignalprocessorstodetectthesoundassociatedwithcutting,climbing,orliftingthefencestructure.Thesystemscanalsobeembeddedwithaspecialaudiochannelthatenablessecuritiesto“listen”toactivityalongeachzoneofthefencefortheprotectionofexistingfencesandstructuresagainstcutting,climbing,orlifting.Theycanalsobefittedtocoiledrazorwirefences.
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.Aradar’scomponentconsistsof(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,360°continuous
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
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,sotheywillmoveontoatargetthatdoesn’thavegoodsecurity.
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,thedigitaluserscouldnowreliablyrecordamonth’sworthofsurveillancecontentsonharddrivebecauseof itshighcompressioncapabilityand lower storagecost.Thedigitally recorded imagesare somuchclearerthantheanalog-recordedimages.Thisleadstotherecognitionprocessimmediatelyimprovingforpolice,privateinvestigators,andotherusersthatusevideosurveillanceforidenti-ficationpurposes.Byusingdigitaltechnology,theimagescouldalsobemanipulatedtofurtherimproveclaritybyaddinglight,enhancingtheimage,zoominginonframes,etc.
◾ Covert surveillance cameras: these cameras look like regular items, to hide its identityasasurveillancecamera, forexample,awallclockinashop,afacingfrontdoorteddybear,andapottedplantattheshop’scorner.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.
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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.
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,with360°areacoverageonasingleplane/axis.Inimagingpointofview,anomnidirectionalvisualizationhasvisualizationcapabilityofa360°fieldofviewaroundthehorizontalplaneorwithvisualfieldthatcoverstheentiresphere.Omnidirectionalvisualizationsystemisimportantinareasthatneedlargevisualfieldcoverage,suchasinpanoramicimagingandinrobotics.Aconventionalimagingtoolnor-mallyhasafieldofviewwiththerangeofafewdegreestomaximumof180°.Itcancaptureonlyasemisphereimagewithlightfallingontotheimagingtool’sfocalpoint.However,ontheotherhand,anomnidirectional imagingtoolcancapture light fromalldirections (surrounded360°fieldofview)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.RANSACisanabbreviationfor“RANdomSampleConsensus.”ThisalgorithmwasfirstpublishedbyFischler
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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.Duetotheomnidirectionalvisualization’sabilitytoobtaina360°view,roboticandcomputervisiontaskscanhavebetterresultsforopticalflowandinfeatureselectionandmatching.
For the mechanical approach, the images captured on a single viewpoint are continuous.Oneexampleistherotatingcamerasystem[24–27].Insuchasystem,thecamerarotatesaroundthecenteroftheprojection.Itgeneratesanomnidirectionalimagefromasingleviewpoint.Theproperorderofimagesobtainedbyrotationis joinedtogethertoacquireapanoramicviewforthescene.AnexampleofrotatingcameraisshowninFigure16.3.Arotatingmotorisrequiredtorotatethevideocamerainordertoscantheomnidirectionalview.However,sinceitisnecessarytorotateavideocamerainafullcircleinordertoacquireasingleomnidirectionalimage,itisimpossibletogeneratereal-timeomnidirectionalimage.Otherdisadvantagesofrotatingcamerasystemarethatitrequirestheuseofmovingpartsandprecisepositioning.Theimagecaptured
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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. 29–38, 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. MATLAB®inthe laptopcomputerwillperformunwarpingonthe imagescaptured intopanoramic form.Thehuman intruderdetection algorithm that is programmed inMATLABwill thenbeusedtoprocess thepanoramic images todetect thepresenceofhumanintruder. Ifhumanintruderisdetected,alarmwillbesignaledandportionsofsuspectedimagewithhumanintruderwillbestoredinadatabaseforfurtheridentificationpurposes.
Thespecificdesignhyperbolicopticalmirrorusedintheomnidirectionalsurveillancesystemisasmall-sizewide-viewtype,withouterdiameterof40mmandangleofview30°abovehorizon-talplanemanufacturedbyACCOWLEVISION.Themirrorcanreflecta360°viewsurroundedbyitself,andasthewebcameraplugsonit,omnidirectionalimageswithinaguardedperimetercanbecapturedandsenttoalaptopcomputerinamonitoringroomtobeprocessedforsurveil-lancepurpose.A custom-madebracket that is shown inFigure16.8 is designed to attach thehyperbolicmirrortothewebcameraviaasocket.
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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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.
16.3.2 Pano-Mapping Table MethodThis method uses a table, which is so-called the pano-mapping table, to process the imageconversion.Pano-mappingtablewillbecreated“onceandforall,”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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wherevanducorrespondtothex-andy-coordinatesoftheomnidirectionalimage.Thiscoor-dinate(u,v)obtainedisinsertedintothepano-mappingtableTMN=Tij.TheuandvwillthenbeprocessedforNtimesbyincreasingjforNtimestoobtaindifferentangles,θ,tolaterdetermineall the coordinates corresponding to the value of landmarkpoint.These coordinates obtainedareinsertedintothetableofi=1withtheircorrespondingj=1toj=N,andtheiwillthenbeincreasedby1,andtheprocessisrepeatedforj=1toj=Ntodetermineallcoordinatesrelatedtoi=2.ThisiwillberepeatedforMtimes,andatableofM×Nentrieswithallthecoordinatescanbegenerated.Thecoordinatesineachoftheentriesaretakenonebyone,inordertomapeachandeverypixelintheomnidirectionalimagewiththecoordinateinthecurrententry,intoanewpanoramicimage.Theconversioniscompletedupontheendofmappingofthetable.
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16.3.4 Performance EvaluationThis subsection reports the performance evaluation for different unwarping methods. Fewimportantfactorsareselectedfortheperformanceevaluationoftheunwarpingmethods.Thesefactorsincluderesolutionoftheimagegenerated,qualityofimage,algorithmusedinperform-
A
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Figure 16.18 Circular sampling structure and the unwarping process.
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Figure 16.17 Process of log-polar mapping.
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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,hencethename“onceandforall.”
5.Processing time:Theprocessingtimeforallthethreeunwarpingmethodstotransformanomnidirectional image into a panoramic image is calculated using MATLAB function“cputime.”Theprogram isprocessedfive timesonfivedifferent images, and theaverageprocessingtimeiscomputed.Itisfoundthatpano-mappingtablemethodhasthefastestcomputationtime,whichis1.220s,followedbylog-polarmappingmethodbeing2.003sand3.426sfortheDGTmethod.
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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methodtendstomaketheobjectsintheimageextendedand“broader”thantheoriginalimage.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.ThisisverygoodinpreservingCPU’smemory,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:
Figure 16.25 (a) Horizontal middle line and the starting point as in step 1. (b) Detection of (c/w from starting point) and (counter c/w from starting point).
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Step 5: Left significant point detection: Search downward along Bl from PHT for thefirstleftmostpointencountered(Plp).Next,searchfortherightmostpointrightafterPlpwhichisPld(refertoFigure16.27forbetterunderstanding):
Step8:Neck–bodyposition test:Calculate∆x,which is thedistancebetweenxcandxmwherexc=horizontalcenterbetweenPldandPrdandxmisobtainedinstep1.Definewn=horizontaldistancebetweenPldandPrd.
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16.4.3 Experimental ResultsIn this section, the application of the proposed omnidirectional human intruder detectionsystem is briefly illustrated. An omnidirectional image captured using digital camera onthe site is shown in Figure 16.29. An omnidirectional thermal image also captured usingthermal camera on the site is shown in Figure 16.30. The unwarped form of Figure 16.29
Figure 16.29 Case studies of trespasser detection (digital color form).
Figure 16.30 Case studies of trespasser detection (thermal image).
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(digitalcolorpanoramicform)isshowninFigure16.31,whereastheunwarpedformofFigure16.30(thermalimagepanoramicform)isshowninFigure16.32,respectively.InFigure16.32,thelog-polarmappingprocessisby4:1reductionmappingscale,whichmeansthat320×240omnidirectional thermal image’s Cartesian pixels are mapped to one-fourth of the thermalimage Cartesian pixels (320×60) in panoramic view, with fourfold data compression com-pared to original omnidirectional thermal image as inFigure 16.30.The captured thermalimagesaretestedfortwotrespasserfaintdetectionalgorithmsasproposedinSections4.1and4.2intheprecedingtext.
16.4.3.1 Experimental Results for Partitioned ROI-Based Human Intruder Detection Algorithm
AsforGvalue,thealgorithmistestedwithhumanmovingtowardandawayfromthecap-tured region with minimum regions that a human being will appear on-screen, G=1–5. Thegraphofaccuracyversusminimumregionsthatahumanbeingappearson-screenGisshowninFigure16.35.Fromthegraph,theoptimumGvalueis3withhighestaccuracyof93.5%.
For testing the trespasserdetectionperformanceofpartitionedROI-basedtrespasserdetec-tion algorithm, a total of10,000 imageswith test subjects (humanbeingor animal) roamingrandomlyinthetestsite(asshowninFigure16.37)visibletotheproposedsystemaretakenassamples.Thisincludesthermalimageswithasingletrespasser,morethanonetrespasser,withoutatrespasser,andanimals(cats,birds,etc.,whicharenotcountedastrespassers).The“operatorperceivedactivity”(OPA)[46]isusedandtheoperatorwillcommentontheimagescaptured,
Theaccuracyoftheproposedalgorithmisthenevaluatedusing“OPA”inwhichthepro-posedalgorithmisevaluatedwithrespecttotheresultsinterpretedbyahumanobserver[46].Firstly, the panoramic images are tested using the proposed algorithm. Then, the result is
60.00%
40.00%
20.00%
0.00%1
Accuracy vs. minimum regions that a humanbeing will appear on screen
2 3 4 5
80.00%
100.00%
Figure 16.35 Accuracy versus minimum regions that a human being will appear on-screen.
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Hence,asecondeffectivehumanintruderdetectionalgorithm,whichisthehumanheadcurvetestalgorithmwithhumanheaddetectioncapability,isproposed.Bycomparingthetwohumanintruderdetectionalgorithms,humanheadcurvetestalgorithmrequiredcomplicatedheadsym-metrictestandcurvetest.PartitionedROI-basedhumanintruderdetectionalgorithmissuper-seding human head curve test algorithm in terms of simplicity and lower computational timeconsumption(averageroutinetimeforprocessingonesampleis1.3sforpartitionedROI-basedhumanintruderdetectionalgorithmand2.27sforhumanheadcurvetestalgorithm).However,intermsofefficiency,humanheadcurvetestalgorithmwithanaccuracyof81.38%ishigherthanpartitionedROI-basedhumanintruderdetectionalgorithmwithanaccuracyof70.8%inthesamesetof10,000testedimages.
16.5 Conclusion and Future Research DirectionsThis chapter presented omnidirectional human intrusion detection system using computervision techniques. Two imaging methods, namely, vision spectrum imaging and IR imag-ing,areappliedincomputervision-basedomnidirectionalhumanintrusiondetectionsystem.Simulationresultsshowthatlog-polarmappingproposedintransformingthecapturedomnidi-rectionalimagesintopanoramicformhasgoodqualityinoutputimagewithhighdatacompres-sionrateandfastprocessingspeedinprovidingobserverorimageprocessingtoolsawideangleof view. Automatic human intrusion detection algorithms are implemented in the proposedomnidirectionalimagingsystem,bothinvisionspectrumimagingandinIRspectrumimaging,respectively.TheproposedhumanintrusionalgorithmincludespartitionedROIalgorithmandhumanheadcurvetestalgorithm.ExperimentalresultsalsoshowthatpartitionedROI-basedhumanintruderdetectionalgorithmissupersedinghumanheadcurvetestalgorithmintermsof simplicity and lower computational time consumption. However, human head curve testalgorithmcantraceouthumanintruderfromthepanoramicimagesmoreaccuratelycomparedtoROIalgorithm.
Currently the omnidirectional human intrusion detection systems are applied in indoorbuildingsecurityfori-habitat(smarthome),fossilpowerplant,etc.,andprototypingforborderintrusiondetection,onhumantargets(includingsmugglers,illegalimmigrants,orterrorists).Inthefuture,itwillbeembeddedwithfacialrecognitioncapabilitiestorecordandidentifycrimi-nals’andsuspects’identity.Also,amobilerobotcanbebuiltformovingaroundthesurveillancesitecarryingsuchomnidirectionalsurveillancesystem.Theimagingtoolpowerisdesignedtobesuppliedbyabatteryinsteadofapowerplug.Itallowstherobottocarrythesurveillanceimagingtoolsetwithoutlimitationofthepowercables’length.Byusingamobilerobot,severalsitescanbemonitoredbyusingonlyoneomnidirectionalsurveillancesystem.Itisalsoaplantoemploymicroprocessor modules such as field programmable gate array (FPGA) and Advanced RISCMachine(ARM)forimageprocessingandanalyzingtasksinsteadofacomputertoeffectivelyreducethecostsandpowerconsumptionoftheproposedsystem.Thesetopicswillbeaddressedinfutureworks.
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ofRGBvalue for aparticular current imagepixel toprevious imagepixel” shouldbemodifiedas“whereQisthethresholdvalueofthedifferenceofthesumofRGBvaluesbetweenaparticularcurrentimagepixelandpreviousimagepixel”.