/
Fast Shapebased Road Sign Detection for a Driver Assistance System Gareth Loy Computer Fast Shapebased Road Sign Detection for a Driver Assistance System Gareth Loy Computer

Fast Shapebased Road Sign Detection for a Driver Assistance System Gareth Loy Computer - PDF document

olivia-moreira
olivia-moreira . @olivia-moreira
Follow
552 views
Uploaded On 2014-12-27

Fast Shapebased Road Sign Detection for a Driver Assistance System Gareth Loy Computer - PPT Presentation

kthse Nick Barnes Autonomous Systems and Sensor Technologies Program National ICT Australia Canberra Australia Email nickbarnesnictacomau Abstract A new method is presented for detecting trian gular square and octagonal road signs ef64257ciently and ID: 30341

kthse Nick Barnes Autonomous Systems

Share:

Link:

Embed:

Download Presentation from below link

Download Pdf The PPT/PDF document "Fast Shapebased Road Sign Detection for ..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Fig.1.Votinglinesassociatedwithagradientelementwhensearchingfordifferentshapes.threshold1,aresettozeroandtheremainingelementsnormalised.Eachremainingnon-zerogradientelementvotesforapotentialcirclecentreadistanceraway(whereristheradiusofthecirclebeingtargeted)alongthelineofthegradientvector.Thevoteisplacedattheclosestpixeltothispoint.Thepointsvotedforarecalledaffectedpixelsandaredenedby:pve(p)=pround(rg(p));whereg(p)istheunitgradientatpointp.Thereareposi-tivep+veandnegativep�veaffectedpixelscorrespondingtopointsthatthegradientpointstowardsandawayfromrespectively.Sincewedonotknowaprioriwhetherasignwillbelighterordarkerthanthebackgroundweusebothpositivelyandnegativelyaffectedpixelsconcurrently.However,ifsuchinformationisknownitcanbeused.Toextendthisvotingschemetoregularpolygonswedenethe`radius'ofapolygonastheperpendiculardistancefromanedgetothecentroid.Further,ratherthangradientelementsvotingforasinglepoint,alineofvotesiscastdescribingpossibleshapecentroidpositionsthatwouldaccountfortheobservedgradientelement.Figure1showsdifferentvotescastbyagradientelementg(p)whensearchingfordifferentshapesatagivenradius(onlythevotesassociatedwiththepositivelyaffectedpixelareshown).Whereasinthecaseofacircleasinglevoteiscastpergradientelement,alineofvotesiscastwhensearchingforstraight-sidedshapes.Thewhitebarsindicatepotentialcentroidlocationsthatreceiveapositivevote,andthedarkbarsindicatelocationsthatreceiveanegativevote.Thenegativevotingisintroducedtoattenuatetheresponsegeneratedbystraightlinestoolongtocorrespondtoshapeedgesatthetargetradius.ThelengthofthelineofpixelsvotedforisdenedbywasshowninFigure2.Thewidthparameterwischosensothateverypointonashapeedgewillcastavoteforthecorrectshapecentroid,andisgivenby:w=roundrtan n;whereristheradiusandnthenumberofsidesofthepolygonbeingtargeted.Thelineonwhichtheaffectedpixelsliecanbeapprox-imatedby:L(p;m)=pve(p)+round(mg(p));1Athresholdequalto5%ofthemaximumpossiblegradientmagnitudewasusedforourexperiments. Fig.2.Lineofpixelsvotedforbyagradientelement Fig.3.Exampleofn-anglegradientprojectedfromapointpwhereg(p)isaunitvectorperpendiculartog(p).Thepixelsreceivingapositivevotearethengivenby:L(p;m)jm2[�w;w];andthosereceivinganegativevoteby:L(p;m)jm2[�2w;�w�1][[w+1;2w]:Whethertargetingcirclesorregularpolygons,allvotesareaccumulatedintoavoteimageOr.Figure4(b)showsanexamplevoteimageforanoctagonaltarget.Regularpolygonsareequiangulari.e.,theirsidesareseparatedbyaregularangularspacing;forann-sidedpolygonthisis360=ndegrees.Toimproveourdetectionoftheseshapesweintroducearotationallyinvariantmeasureofhowwellasetofedgestsaparticularangularspacing.Dene (x;y)=n(x;y)where=6 gisthegradientangle,andnisthenumberofsidesofthetargetpolygon.Letvbetheunitvectoreldsuchthat6 v(x;y)= (x;y).Foragivensetofedgepointspi,themagnitudeofthevectorsumPiv(pi)indicateshowwellthesetofedgesg(pi)tstheangularspacingdenedbyn.ConsidertheexampleinFigure5.Threeedgepointspiaresampledfromthesidesofanequilateraltriangle.Theunitgradientvectorsandtheirassociatedanglesareshownin(a).Bymultiplyingthegradientanglesbyn(n=3foratriangle)theresultingvectorssharethesamedirectionif,andonlyif,theiroriginalorientationswerespaced360=ndegreesapart.Thusthemagnitudeofthevectorsumofthesen-anglevectorsismaximaliftheedgepointsoccuratthetargetedangularspacing.Toutilisethisresultweconstructavectoreldofprojectedn-anglegradientsbyconsideringeachnon-zeroelementofgandprojectingitsassociatedn-anglevectorv(p)ontoitsvotingspaceasshowninFigure3(notethesignisreversedwhenprojectingontonegativelyvotedpixels).Vectorsprojectedontothesamepixelaresummed.TheresultisavectoreldBr,whosemagnitudeindicateshowwellthegradientelementsvotingoneachpointmatchthetargetangularspacing.Figure4(c)showsanexampleofsuchamagnitudeimageforanoctagonaltarget.Forincreasingnthen-anglerepresentationislimitedbytheaccuracyofthegradientorientationestimate.However,itisperfectlyadequateforn=8(e.g.Figure4(c))whichisthemaximumrequiredforroadsigndetection. Fig.8.Resultsforsearchingfortriangles(toprow)withr2f8;10;::;18g,andsquares(middlerow)andoctagons(bottomrow)withr2f10;12;14;17;20;25g.Alltargetedsignsarecorrectlydetected. (a) (b) (c) (d)Fig.9.Difculttests[3]D.H.Ballard,“Generalizingthehoughtransformtodetectarbitraryshapes,”PatternRecognition,vol.13,no.2,pp.111–122,1981.[4]R.Strzodka,I.Ihrke,andM.Magnor,“Agraphicshardwareimplementationofthegeneralizedhoughtransformforfastobjectrecognition,scaleand3dposedetection,”inProc12thIntConfonImageAnalysisandProcessing,Mantova,Italy,2003,pp.188–193.[5]G.GuyandG.Medioni,“Inferringglobalperceptualcontoursfromlocalfeatures,”InternationalJournalofComputerVision,vol.20,no.1-2,pp.113–333,Oct.1996.[6]N.M.BarnesandZ.Q.Liu,“Embodiedcategorisationforvision-guidedmobilerobots,”PatternRecognition,vol.37,no.2,pp.299–312,Feb.2004.[7]M.BetkeandN.Makris,“Fastobjectrecognitioninnoisyimagesusingsimulatedannealing,”A.I.Lab,M.I.T.,Cambridge,Mass,USA,Tech.Rep.AIM-1510,1994.[8]D.M.Gavrila,“Aroadsignrecognitionsystembasedondynamicvisualmodel,”inProc14thInt.Conf.onPatternRecognition,vol.1,Aug1998,pp.16–20.[9]P.Paclik,J.Novovicova,P.Somol,andP.Pudil,“Roadsignclassicationusingthelaplacekernelclassier,”PatternRecognitionLetters,vol.21,pp.1165–1173,2000.[10]J.Miura,T.Kanda,andY.Shirai,“Anactivevisionsystemforreal-timetrafcsignrecogntition,”inProc2000IEEEIntVehiclesSymposium,Oct2002,pp.52–57.[11]B.Johansson,“Roadsignrecognitionfromamovingvehicle,”Master'sthesis,CentreforImageAnalysis,SwedishUniversityofAgriculturalSciences,2002.[12]L.Priese,J.Klieber,R.Lakmann,V.Rehrmann,andR.Schian,“Newresultsontrafcsignrecognition,”inProceedingsoftheIntelligentVehiclesSymposium.Paris:IEEEPress,Aug.1994,pp.249–254.[Online].Available:citeseer.nj.nec.com/priese94new.html[13]G.Piccioli,E.D.Micheli,P.Parodi,andM.Campani,“Robustmethodforroadsigndetectionandrecognition,”ImageandVisionComputing,vol.14,no.3,pp.209–223,1996.[14]C.Y.Fang,C.S.Fuh,S.W.Chen,andP.S.Yen,“Aroadsignrecognitionsystembasedondynamicvisualmodel,”inProcIEEEConf.onComputerVisionandPatternRecognition,vol.1,2003,pp.750–755.[15]D.G.Shaposhnikov,L.N.Podladchikova,A.V.Golovan,andN.A.Shevtsova,“Aroadsignrecognitionsystembasedondynamicvisualmodel,”inProc15thIntConfonVisionInterface,Calgary,Canada,2002.[16]S.-H.HsuandC.-L.Huang,“Roadsigndetectionandrecognitionusingmatchingpursuitmethod,”ImageandVisionComputing,vol.19,pp.119–129,2001.[17]R.Labayrade,D.Aubert,andJ.-P.Tarel,“Realtimeobstacledetectioninstereovisiononnonatroadgeometrythroughv-disparityrepresentation,”inProcIEEEIntVehiclesSymposium,June2002.