METHODSAND TECHNIQUESFOR
A.ENISÇETIN
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Camera-BasedTechniques
2.8
Part1
Thefirststepinvideofiredetection(VFD)istoapplyabackgroundsubtractionalgorithmtoextractmovingregionsinthevideo.Thenthedetected regionsareanalyzedtemporallytobeclassifiedintermsofflickeringcharacteristics.Markovmodelsandfrequencydomaintechniquescanbeusedto identifyiftheflickeringcharacteristicsbelongtoflames.Inthenextstep, spatialanalysisisperformedtocheckfortheirregularitiesthatareusedto identifyflames.
Anothermethodistoextractfeaturesfromthemovingregionsand useclassifierswhoaretrainedofflinewithvideosoffireandfalsealarm sources.Itisalsopossibletouseac tivelearningalgorithmswhichare updatedonlinetoclassifyflameregions.Themostimportantproblem withvisiblerangefiredetectionsisthefalsealarms.Fire-coloredmoving regionscanbedifficulttodistinguishfromtheactualflames.Firedetectionalgorithmsaregenerallydevelopedforstationarycameras.When movingcamerasareused,itbecomesdifficulttodetecttheflickering characteristicsofflames.Detectionoffireusingamovingcameraisa futureresearchproblem.Anotherapp licationofvideo-basedfiredetectionissmokedetectionforearlywildfirewarningsystems.Forestsare usuallymonitoredusingPTZcamerasth atscanrecordedpresetpositions inaspecificorder.Thesecamerascan monitorlargerdistancesthanusual visiblerangecameras.Inwildfiredetectionapplications,smokebecomes visiblebeforetheflames;thereforeitmakessensetofocusonsmoke detectionforthesesystems.
Withthedecreasedcostofinfraredsensors,itbecamepossibletouselongandshort-waveinfraredcamerasforflamedetection.Sinceinfrared(thermal) camerasformimageswhoseintensitydependonthetemperatureofthe objects,theycouldbeusedtoreducemostofthefalsealarms.Inmostcases, thetemperatureofflamesishigherthanthesurroundingenvironmentandIR camerascansuccessfullydetecttheflickeringflames.Initially,infraredflame detectionalgorithmsprocessednearinfraredimages(NIR)toverifytheexistenceofflames.Morerecentmethodsstartedtouseshort-andlong-wave infrared(SWIR,LWIR)thermalcamerasforfireandflamedetection.
ThischapterisbasedontheVFDsurveypaperin[1].RecentlyproposedVFDtechniquesareviablealternativesorcomplementstoexisting firedetectiontechniquesandhaveshowntobeusefulinsolvingseveral problemsrelatedtothetraditionalsensors.Conventionalsensorsaregenerallylimitedtoindoorsandarenotapplicableinlargeopenspacessuchas shoppingcenters,airports,carparks,andforests.Theyrequireacloseproximitytothefireandmostofthemcannotprovideadditionalinformation aboutfirelocation,dimension,etc.Oneofthemainlimitationsofcommerciallyavailablefirealarmsystemsisthatitmaytakealongtimeforcarbonparticlesandsmoketoreachthe“point”detector.Thisiscalledthe “transportdelay.”Itisourbeliefthatvideoanalysiscanbeappliedinconditionsinwhichconventionalmethodsfail.VFDhasthepotentialtodetect thefirefromadistanceinlargeopenspacesbecausecamerascanmonitor
“volumes.”Asaresult,VFDdoesnothavethetransportandthreshold delayfromwhichthetraditional“point”sensorssuffer.Assoonassmoke orflamesoccurinoneofthecameraviews,itispossibletodetectfire immediately.Weallknowthathumanbeingscandetectanuncontrolled fireusingtheireyesandvisionsystems,butaspointedoutabove,itisnot easytoreplicatehumanintelligence.
Theresearchinthisdomainwasstartedinthelatenineties.Mostofthe VFDarticlesavailableintheliteratureareinfluencedbythenotionof “weak”ArtificialIntelligence(AI)frameworkwhichwasfirstintroduced byHubertL.Dreyfusinhiscritiqueofthe“generalized”AI[3,4].Dreyfus presentssolidphilosophicalandscientificargumentsonwhythesearchfor “generalized”AIisfutile[5].Therefore,eachspecificproblemincluding VFDfireshouldbeaddressedasanindividualengineeringproblemwhich hasitsowncharacteristics[6].Itispossibletoapproximatelymodelthefire behaviorinvideousingvarioussignalandimageprocessingmethodsand automaticallydetectfirebasedontheinformationextractedfromvideo. However,thecurrentsystemssufferfromfalsealarmsbecauseofmodeling andtraininginaccuracies.
CurrentlyavailableVFDalgorithmsmainlyfocusonthedetectionand analysisofsmokeandflamesinconsecutivevideoimages.Inearlyarticles, mainlyflamedetectionwasinvestigated.Recently,thesmokedetection problemisalsoconsidered.Thereasonforthiscanbefoundinthefact thatsmokespreadsfasterandinmostcaseswilloccurmuchfasterinthe fieldofviewofthecameras.Inwildfireapplications,itmaynotevenbe possibletoobserveflamesforalongtime.Themajorityofthestate-ofthe-artdetectiontechniquesfocusesonthecolorandshapecharacteristics, togetherwiththetemporalbehaviorofsmokeandflames.However,due tothevariabilityofshape,motion,transparency,colors,andpatternsof smokeandflames,manyoftheexistingVFDapproachesarestillvulnerable tofalsealarms.Duetonoise,shadows,illuminationchanges,andother visualartifactsinrecordedvideosequences,developingareliabledetection systemisachallengetotheimageprocessingandcomputervision community.
Withtoday’stechnology,itisnotpossibletohaveafullyreliableVFD systemwithoutahumanoperator.However,currentsystemsareinvaluable toolsforsurveillanceoperators.Itisalsoourstrongbeliefthatcombining multi-modalvideoinformationusingbothvisibleandinfrared(IR)technologywillleadtohigherdetectionaccuracy.Eachsensortypehasitsown
specificlimitations,whichcanbecompensatedbyothertypesofsensors. Although,itwouldbedesirabletodevelopafiredetectionsystemwhich couldoperateontheexistingclosedcircuittelevision(CCTV)equipment withoutintroducinganyadditionalcost.However,thecostofusingmultiplevideosensorsdoesnotoutweighthebenefitofmulti-modalfireanalysis.ThefactthatIRmanufacturersalsoensureadecreaseinthesensorcost inthenearfuturefullyopensthedoortomulti-modalvideoanalysis.VFD camerascanalsobeusedtoextractusefulrelatedinformation,suchasthe presenceofpeoplecaughtinthefire,firesize,firegrowth,smoke direction,etc.
VFDsystemscanbeclassifiedintovarioussubcategoriesaccordingto (i)thespectralrangeofthecameraused, (ii)thepurpose(flameorsmokedetection), (iii)therangeofthesystem. Thereareoverlapsbetweenthecategoriesabove.
2.1VFDINVISIBLE/VISUALSPECTRALRANGE
Overthelastyears,thenumberofpapersaboutvisualfiredetectioninthe computervisionliteraturehasgrownexponentially[2].Asis,thisrelatively newsubjectinvisionresearchisinfullprogressandhasalreadyproduced promisingresults.However,thisisnotacompletelysolvedproblem,aswith mostcomputervisionproblems.Behaviorofsmokeandflamesofanuncontrolledfiredifferswithdistanceandillumination.Furthermore,camerasare notcolorand/orspectralmeasurementdevices.Theyhavedifferentsensors andcolorandilluminationbalancingalgorithms.Theymayproducedifferentimagesandvideoforthesamescenebecauseoftheirinternalsettingsand algorithms.
Inthissection,achronologicaloverviewofthestate-of-the-art(ie,acollectionoffrequentlyreferencedpapersonshortrange[<100m])firedetectionmethodsispresentedinthetablesbelow.Foreachofthesepapers,we investigatedtheunderlyingalgorithmsandcheckedtheappropriatetechniques.Inthefollowing,wediscusseachofthesedetectiontechniques andanalyzetheiruseinthelistedpapers.
State-of-the-art:underlyingtechniques(PART1:2002-2007).
Xiong [18]
State-of-the-art:underlyingtechniques(PART2:2007-2009).
[20]
[21]
[23]
[25]
[26]
State-of-the-art:underlyingtechniques(PART3:2010-2011).
2.1.1ColorDetection
ColordetectionwasoneofthefirstdetectiontechniquesusedinVFDandis stillusedinalmostalldetectionmethods.Themajorityofthecolor-based approachesinVFDmakeuseofRGBcolorspace,sometimesincombinationwithHSI/HSVsaturation[10,24,27,28].Themainreasonforusing RGBisthatalmostallvisiblerangecamerashavesensorsdetectingvideo inRGBformatandthereistheobviousspectralcontentassociatedwiththis colorspace.ItisreportedthatRGBvaluesofflamepixelsareintheredyellowcolorrangeindicatedbytherule(R > G > B)asshownin Fig.2.1. Similarly,insmokepixels,R,G,andBvaluesareveryclosetoeachother. Morecomplexsystemsuserule-basedtechniquessuchasGaussiansmoothed colorhistograms[7],statisticallygeneratedcolormodels[15],andblending functions[20].Itisobviousthatcolorcannotbeusedbyitselftodetectfire becauseofthevariabilityincolor,density,lighting,andbackground. However,thecolorinformationcanbeusedasapartofamoresophisticated system.Forexample,chrominancedecreaseisusedinsmokedetection
Figure2.1 Colordetection:smokeregionpixelshavecolorvaluesthatareclosetoeach other.Pixelsofflameregionslieinthered-yellowrangeofRGBcolorspacewith R > G > B.
schemesofRefs.[14,2].Luminancevalueofsmokeregionsshouldbehigh formostsmokesources.Ontheotherhand,thechrominancevaluesshould beverylow.
TheconditionsinYUVcolorspaceareasfollows:
Condition1: Y > TY
Condition2: U 128 jj < TU & jV 128j < TV . where Y, U,and V aretheluminanceandchrominancevaluesofaparticular pixel,respectively.Theluminancecomponent Y takesvaluesintherange [0,255]inan8-bitquantizedimageandthemeanvaluesofchrominance channels U and V areincreasedto128sothattheyalsotakevaluesbetween 0and255.Thethresholds TY, TU,and TV areexperimentallydetermined[37].
2.1.2MovingObjectDetection
MovingobjectdetectionisalsowidelyusedinVFDbecauseflamesand smokearemovingobjects.Todetermineifthemotionisduetosmoke oranordinarymovingobject,furtheranalysisofmovingregionsinvideo isnecessary.
Well-knownmovingobjectdetectionalgorithmsarebackground(BG) subtractionmethods[16,21,18,14,13,17,20,22,27,28,30,34],temporal differencing[19],andopticalflowanalysis[9,8,29].Theycanallbeused aspartofaVFDsystem.
Inbackgroundsubtractionmethods,itisassumedthatthecameraisstationary.In Fig.2.2,abackgroundsubtraction-basedmotiondetection
exampleisshownusingthedynamicbackgroundmodelproposedbyCollinsetal.[38].ThisGaussianMixtureModel-basedapproachmodelwas usedinmanyofthearticleslistedintablesabove.
SomeoftheearlyVFDarticlessimplyclassifiedfire-coloredmoving objectsasfirebutthisapproachleadstomanyfalsealarms,becausefalling leavesinautumnorfire-coloredordinaryobjects,etc.,mayallbeincorrectly classifiedasfire.Furtheranalysisofmotioninvideoisneededtoachieve moreaccuratesystems.
2.1.3MotionandFlickerAnalysisUsingFourierandWavelet Transforms
Asitiswellknown,flamesflickerinuncontrolledfires,thereforeflicker detection[24,18,12,13,27,28,30]invideoandwavelet-domainsignal energyanalysis[21,14,20,26,31,39]canbeusedtodistinguishordinary objectsfromfire.Thesemethodsfocusonthetemporalbehaviorofflames andsmoke.Asaresult,flamecoloredpixelsappearanddisappearattheedges ofturbulentflames.Theresearchin[16,18]showsexperimentallythatthe flickerfrequencyofturbulentflamesisaround10Hzandthatitisnotgreatly affectedbytheburningmaterialandtheburner.Asaresult,usingfrequency analysistodifferentiateflamesfromothermovingobjectsisproposed.However,anuncontrolledfireinitsearlystageexhibitsatransitiontochaosdue tothefactthatthecombustionprocessconsistsofnonlinearinstabilities whichresultintransitiontochaoticbehaviorviaintermittency[40–43]. Consequently,turbulentflamescanbecharacterizedasachaoticwide-band frequencyactivity.Therefore,itisnotpossibletoobserveasingleflickering frequencyinthelightspectrumduetoanuncontrolledfire.ThisphenomenonwasobservedbyindependentresearchersworkingonVFDand methodswereproposedaccordingly[14,44,27].Similarly,itisnotpossible totalkaboutaspecificflickerfrequencyforsmoke,butweclearlyobservea time-varyingmeanderingbehaviorinuncontrolledfires.Therefore,smoke flickerdetectiondoesnotseemtobeaveryreliabletechnique,butitcanbe usedaspartofamulti-featurealgorithmfusingvariousvisioncluesforsmoke detection.TemporalFourieranalysiscanstillbeusedtodetectflickering flames,butwebelievethatthereisnoneedtodetectspecifically10Hz. AnincreaseinFourierdomainenergyin510Hzisanindicatorofflames. Thetemporalbehaviorofsmokecanbeexploitedbywaveletdomain energyanalysis.Assmokegraduallysoftenstheedgesinanimage,Toreyin etal.[14]foundtheenergyvariationbetweenbackgroundandcurrent imageasacluetodetectthepresenceofsmoke.Inordertodetecttheenergy
decreaseinedgesoftheimage,theyusetheDiscreteWaveletTransform (DWT).TheDWTisamulti-resolutionsignaldecompositionmethod obtainedbyconvolvingtheintensityimagewithfilterbanks.Astandard halfbandfilterbankproducesfourwaveletsubimages:theso-calledlowlowversionoftheoriginalimage Ct,andthehorizontal,vertical,anddiagonalhighfrequencybandimages Ht, Vt,and Dt.Thehigh-bandenergyfrom subimages Ht, Vt,and Dt isevaluatedbydividingtheimage It inblocks bk of arbitrarysizeasfollows:
Sincecontributionofedgesaremoresignificantinhighbandwavelet imagescomparedtoflatareasoftheimage,itispossibletodetectsmoke usingthedecreasein E(I t, b k).Astheenergyvalueofaspecificblock variessignificantlyovertimeinthe presenceofsmoke,temporalanalysis oftheratiobetweenthecurrentinputframewaveletenergyandthe backgroundimagewaveletenergyisusedtodetectthesmokeasshown in Fig.2.3
Figure2.3 DWT-basedvideosmokedetection:Whenthereissmoke,theratiobetween theinputframewaveletenergyandtheBGwaveletenergydecreasesandshowsahigh degreeofdisorder.
2.1.4SpatialWaveletColorVariationandAnalysis
Flamesofanuncontrolledfirehavevaryingcolorsevenwithinasmallarea. Spatialcolordifferenceanalysis[24,13,28,32]focusesonthischaracteristic. Usingrangefilters[24],variance/histogramanalysis[32],orspatialwavelet analysis[13,28],thespatialcolorvariationsinpixelvaluesareanalyzedto distinguishordinaryfire-coloredobjectsfromuncontrolledfires.In Fig.2.4, theconceptofspatialdifferenceanalysisisfurtherexplainedbymeansofa histogram-basedapproach,whichfocusesonthestandarddeviationofthe greencolorband.ItwasobservedbyQiandEbert[24]thatthiscolorband isthemostdiscriminativebandforrecognizingthespatialcolorvariationof flames.Thiscanalsobeseenbyanalyzingthehistograms.Greenpixelvalues varymorethanredandbluevalues.Ifthestandarddeviationofthegreen colorbandexceeds tσ ¼ 50(:Borges[32])inatypicalcolorvideotheregion islabeledasacandidateregionforaflame.Forsmokedetection,ontheother hand,experimentsrevealedthatthesetechniquesarenotalwaysapplicable becausesmokeregionsoftendonotshowashighspatialcolorvariationas flameregions.Furthermore,texturedsmoke-coloredmovingobjectsare difficulttodistinguishfromsmokeandcancausefalsedetections.Ingeneral, smokeinanuncontrolledfireisgrayanditreducesthecolorvariationinthe background.Therefore,inYUVcolorspaceweexpecttohavereductionin thedynamicrangeofchrominancecolorcomponentsUandVafterthe appearanceofsmokeintheviewingrangeofcamera.
Figure2.4 Spatialdifferenceanalysis:incaseofflames,thestandarddeviation σ G ofthe greencolorbandoftheflameregionexceeds tσ ¼ 50(:Borges[32]).
2.1.5DynamicTextureandPatternAnalysis
Adynamictextureorpatterninvideo,suchassmoke,flames,water,and leavesinthewind,canbesimplydefinedasatexturewithmotion [45,46](ie,aspatiallyandtime-varyingvisualpatternthatformsanimage sequenceorpartofanimagesequencewithacertaintemporalstationarity) [47].Althoughdynamictexturesareeasilyobservedbyhumaneyes,theyare difficulttodiscernusingcomputervisionmethodsasthespatiallocationand extentofdynamictexturescanvarywithtimeandtheycanbepartiallytransparent.Somedynamictextureandpatternanalysismethodsinvideo [29,33,35]arecloselyrelatedtospatialdifferenceanalysis.Recently,these techniqueshavealsobeenappliedtotheflameandsmokedetectionproblem [46].Currently,awidevarietyofmethodsincludinggeometric,modelbased,statistical,andmotion-basedtechniquesareusedfordynamictexture detection[48–50].
In Fig2.5,dynamictexturedetectionandsegmentationexamplesare shown,usingvideoclipsfromtheDynTexdynamictextureandBilkentdatabases[51,52,50,47].Contoursofdynamictextureregions(eg,fire,water,and steam)areshowninthisfigure.Dynamicregionsinvideoseemtobesegmentedverywell.However,duetothehighcomputationalcost,thesegeneraltechniquesarenotusedinpracticalfiredetectionalgorithmswhichshould runonlow-costcomputers,FPGAs,ordigitalsignalprocessors.Iffuture developmentsincomputersandgraphicsacceleratorscouldlowerthecomputationalcost,dynamictexturedetectionmethodsmaybeincorporatedinto thecurrentlyavailableVFDsystemstoachievemorereliablesystems.
Ordinarymovingobjectsinvideo,suchaswalkingpeople,haveapretty stableoralmostperiodicboundaryovertime.Ontheotherhand,uncontrolledflameandsmokeregionsexhibitchaoticboundarycontours.Therefore,disorderanalysisofboundarycontoursofamovingobjectisusefulfor firedetection.Someexamplesoffrequentlyusedmetricsarerandomnessof areasize[23,32],boundaryroughness[14,11,28,32],andboundaryarea
Figure2.5 Dynamictexturedetection:contoursofdetecteddynamictextureregions areshowninthefigure(ResultsfromDYNTEXandBilkentdatabases [51,53]).
disorder[18].Althoughthosemetricsdifferindefinition,theoutcomeof eachofthemisalmostidentical.Inthesmokedetectordevelopedby Verstocketal.[2],disorderanalysisoftheBoundaryAreaRoughness (BAR)isused,whichisdeterminedbyrelatingtheperimeteroftheregion tothesquarerootofthearea(Fig.2.6).AnothertechniqueisthehistogrambasedorientationaccumulationbyYuan[22].Thistechniquealsoproduces gooddisorderdetectionresults,butitiscomputationallymorecomplex thantheformermethods.Relatedtothedisorderanalysisisthegrowing ofsmokeandflameregionsintheearlystageofafire.In[31,34],thegrowth rateoftheregion-of-interestisusedasafeatureparameterforfiredetection. Comparedtodisordermetrics,however,growthanalysisislesseffectivein detectingthesmoke,especiallyinwildfiredetection.Thisisbecausethe smokeregionappearstogrowveryslowlyinwildfireswhentheyareviewed fromlongdistances.Furthermore,anordinaryobjectmaybeapproaching thecamera.
2.2SPATIOTEMPORALNORMALIZEDCOVARIANCE DESCRIPTORS
Arecentapproachwhichcombinescolorandspatiotemporalinformationby regioncovariancedescriptorsisusedinEuropeanCommissionfundedFP-7 FIRESENSEproject[54–56].Themethodisbasedonanalyzingthespatiotemporalblocks.Theblocksareobtainedbydividingthefire-andsmokecoloredregionsinto3Dregionsthatoverlapintime.Classificationofthe featuresisperformedonlyatthetemporalboundariesofblocksinsteadof
Figure2.6 Boundaryarearoughnessofconsecutiveflameregions.
performingitateachframe.Thisreducesthecomputationalcomplexityof themethod.
CovariancedescriptorsareproposedbyTuzel,Porikli,andMeertobe usedinobjectdetectionandtextureclassificationproblems[54,55].In [57,75,76]temporallyextendednormalizedcovariancedescriptorstoextract featuresfromvideosequencesareproposed.
Temporallyextendednormalizedcovariancedescriptorsaredesignedto describespatiotemporalvideoblocks.Let I(i, j, n)betheintensityof(i, j)th pixelofthe nthimageframeofaspatiotemporalblockinvideo.Thepropertyparametersdefinedinequationsbelowareusedtoformacovariance matrixrepresentingspatialinformation.Inadditiontospatialparameters, temporalderivatives, It and Itt areintroducedwhicharethefirstandsecond derivativesofintensitywithrespecttotime,respectively.Byaddingthese twofeaturestothepreviouspropertyset,normalizedcovariancedescriptors canbeusedtodefinespatiotemporalblocksinvideo.
Forflamedetection:
Inordertoimprovethedetectionperformance,themajorityofthearticlesintheliteratureuseacombinationofthefirefeatureextractionmethods describedabove.Dependingonthefire/environmentalcharacteristics,one combinationoffeatureswilloutperformtheother,andviceversa.In Section2.4,wedescribeanadaptivefusionmethodcombiningtheresultsof variousfiredetectionmethodsinanonlinemanner.
Itshouldbepointedoutthatarticlesintheliteratureandthosewhichare referencedinthisstate-of-the-artreviewindicatethatordinaryvisiblerange camera-baseddetectionsystemspromisegoodfiredetectionresults.However, theystillsufferfromasignificantamountofmisseddetectionsandfalsealarms inpracticalsituations,asinothercomputervisionproblems[5,6].Themain causeoftheseproblemsisthefactthatvisualdetectionisoftensubjecttoconstraintsregardingthesceneunderinvestigation(eg,changingenvironmental conditions,differentcameraparameters,andcolorsettingsandillumination). Itisalsoimpossibletocomparethearticleswitheachotheranddeterminethe bestone.Thisisbecausetheyusedifferenttraininganddatasets.
Adatasetoffireandnon-firevideosisavailabletotheresearchcommunity inEuropeanCommissionfundedFIRESENSEprojectwebpage[56].These testvideoswereusedfortrainingandtestingpurposesofthesmokeandflame detectionalgorithmsdevelopedwithintheFIRESENSEproject.Thus,afair comparisonofthealgorithmsdevelopedbyindividualpartnerscouldbeconducted.Thetestdatabaseincludes27testand29trainingsequencesofvisible spectrumrecordingsofflamescenes,15testand27trainingsequencesofvisiblespectrumrecordingsofsmokescenes,and22testand27training sequencesofvisiblespectrumrecordingsofforestsmokescenes.Thisdatabase iscurrentlyavailabletoregisteredusersoftheFIRESENSEwebsite[Reference:FIRESENSEprojectFileRepository, http://www.firesense.eu,2012].
2.3CLASSIFICATIONTECHNIQUES
Apopularapproachfortheclassificationofthemulti-dimensionalfeature vectorsobtainedfromeachcandidateflameorsmokeblobisSVMclassification,typicallywithRadialBasisFunction(RBF)kernels.Alargenumber offramesoffireandnon-firevideosequencesneedtobeusedfortraining theseSVMclassifiers;otherwisethenumberoffalsealarms(falsepositivesor truenegatives)maybesignificantlyincreased.
OtherclassificationmethodsincludetheAdaBoostmethod[22],neural networks[29,35],Bayesianclassifiers[30,32],Markovmodels[28,33],and rule-basedclassification[58].
Asinanyvideoprocessingmethod,morphologicaloperations,subblocking,andclean-uppost-processing,suchasmedian-filtering,areused asanintegralpartofanyVFDsystem[21,22,25,20,26,33,36,59].
2.4EVALUATIONOFVISIBLERANGEVFDMETHODS
AnevaluationofdifferentvisiblerangeVFDmethodsispresentedin Table2.1. Table2.1 summarizescomparativedetectionresultsforthesmoke andflamedetectionalgorithmbyVerstockt[2](Method1),acombination oftheflamedetectionmethodbyCeliketal.[60]andthesmokedetection byToreyinetal.[14](Method2)andacombinationofthefeature-based flamedetectionmethodbyBorgesetal.[23]andthesmokedetection methodbyXiongetal.[18](Method3).Amongvariousalgorithms, Verstockt’smethodisarelativelyrecentone,whereasflamedetection methodsbyCelikandBorgesandthesmokedetectionmethodsbyToreyin andXiongarecommonlyreferencedmethodsintheliterature.
Table2.1 AnevaluationofdifferentvisiblerangeVFDmethods
(#frames) #Fireframes groundtruth
