文献翻译——建立一个自动车辆车牌识别系统

第1页外文文献资料BuildinganAutomaticVehicleLicense-PlateAbstractRecognitionSystemAbstractDuetoahugenumberofvehicles,moderncitiesneedtoestablisheffectivelyautomaticsystemsfortrafficmanagementandscheduling.OneofthemostusefulsystemsistheVehicleLicense-Plate(VLP)RecognitionSystemwhichcapturesimagesofvehiclesandreadtheseplatesregistrationnumbersautomatically.Inthispaper,wepresentanautomaticVLPRecognitionSystem,ISeeCarRecognizer,toreadVietnameseVLPsregistrationnumbersattraffictolls.Oursystemconsistsofthreemainmodules:VLPdetection,platenumbersegmentation,andplatenumberrecognition.InVLPdetectionmodule,weproposeanefficientboundaryline-basedmethodcombiningtheHoughtransformandContouralgorithm.Thismethodoptimizesspeedandaccuracyinprocessingimagestakenfromvariouspositions.Then,weusehorizontalandverticalprojectiontoseparateplatenumbersinVLPsegmentationmodule.Finally,eachplatenumberwillberecognizedbyOCRmoduleimplementedbyHiddenMarkovModel.Thesystemwasevaluatedintwoempiricalimagesetsandhasproveditseffectiveness(seesectionIV)whichisapplicableinrealtraffictollsystems.ThesystemcanalsobeappliedtosomeothertypesofVLPswithminorchanges.I.INTRODUCTIONTheproblemofVLPrecognitionisaveryinterestingbutdifficultone.Itisveryusefulformanytrafficmanagementsystems.VLPrecognitionrequiressomecomplextasks,suchasVLPdetection,segmentationandrecognition.Thesetasksbecomemoresophisticatedwhendealingwithplateimagestakeninvariousinclinedanglesorplateimageswithnoise.Becausethisproblemisusuallyusedinreal-timesystems,itrequiresnotonlyaccuracybutalsofastprocessing.MostVLPrecognitionapplicationsreducethecomplexityby第2页establishingsomeconstrainsonthepositionanddistancefromthecameratovehicles,andtheinclinedangles.Bythatway,therecognitionrateofVLPrecognitionsystemshasbeenimprovedsignificantly.Inaddition,wecangainmoreaccuracybyusingsomespecificfeaturesoflocalVLPs,suchasthenumberofcharacters,thenumberofrowsinaplate,orcolorsofplatebackground,ortheratioofwidthtoheightofaplate.II.RELATEDWORKTheproblemofautomaticVLPrecognitionhasbeenstudiedsince1990s.Thefirstapproachwasbasedoncharacteristicsofboundarylines.Theinputimagewasfirstprocessedtoenrichboundarylinesinformationbysomealgorithmssuchasthegradientfilter,andresultedinanedgingimage.Thisimagewasbinarizedandthenprocessedbycertainalgorithms,suchasHoughtransform,todetectlines.Eventually,couplesof2-parallellineswereconsideredasaplate-candidate45.Anotherapproachwasmorphology-based2.Thisapproachfocusesonsomepropertiesofplateimagessuchastheirbrightness,symmetry,angles,etc.Duetotheseproperties,thismethodcandetectthesimilarpropertiesinacertainimageandlocatethepositionoflicenseplateregions.Thethirdapproachwastexture-based3.Inthisapproach,aVLPwasconsideredasanobjectwithdifferenttexturesandframes.Thetexturewindowframesofdifferentsizeswereusedtodetectplate-candidates.Eachcandidatewaspassedtoaclassifiertoconfirmwhetheritisaplateornot.Thisapproachwascommonlyusedinfindingtextinimagestasks.Inaddition,therehavebeenanumberofothermethodsrelatingtothisproblemfocusingondetectingVLPinvideodata.III.THEPROPOSEDSYSTEMOursystem,ISeeCarRecognizer,consistsoffourmodules:Pre-processing,VLPdetection,charactersegmentation,andopticalcharacterrecognition(OCR),inwhichthelastthreemodulesdealwiththreemainproblemsofaVLPrecognitiondomain.TheVLPdetectionmodulereceivesimageswhichhavebeenprocessedbythepreprocessingmodulethefirstinputmoduleofthissystem.Theresultedimagesofthismodulearesenttothesegmentationmodule.Thesegmentationmodulesegmentsplate-imagesintoseparatecharacterimages.Thesecharacter-imagesarethenrecognizedbythe第3页OCRmoduleandthefinalresultsareASCIIcharactersandnumbersinplates.A.PreprocessingImagestakenfromcamerawereprocessedbythepreprocessingmodule.Thepurposeofthismodulewastoenrichtheedgefeatures.Becauseourdetectionmethodbasesontheboundaryfeatures,itwillimprovethesuccessfulrateoftheVLPdetectionmodule.Thealgorithmssequentiallyusedinthismodulearegraying,normalizingandhistogramequalization.Afterhavingobtainedagreyscaleimage,weuseSobelfilterstoextracttheedgingimage,andthenthresholdstheimagetoabinaryone.Weusedthelocaladaptivethresholdingalgorithmforthebinarizationstep.Especially,wedevelopanalgorithmbasedondynamicprogrammingtooptimizeitsspeedandmakeitsuitabletoreal-timeapplications1.TheresultedimagesareusedasinputsfortheVLPdetectionmodule.B.VLPDetectionAlgorithmInboundary-basedapproach,themostimportantstepistodetectboundarylines.OneofmostefficientalgorithmsisHoughtransformapplyingtothebinaryimagetoextractlinesfromobject-images.Thenwelookfortwoparallellines,whosethecontainedregionisconsideredplatecandidates.However,thedrawbackofthisapproachisthattheexecutiontimeoftheHoughtransformrequirestoomuchcomputationwhenbeingappliedtoabinaryimagewithgreatnumberofpixels.Especially,thelargerimagetheslowerthealgorithmis.ThespeedofthealgorithmmaybeimprovedbythinningimagebeforeapplyingtheHoughtransform.Nevertheless,thethinningalgorithmisalsoslow.Thislimitationmakestheapproachunsuitableforrealtimetrafficmanagementsystems.ThealgorithmweusedinthissystemisthecombinationoftheHoughTransformandContouralgorithmwhichproduceshigheraccuracyandfasterspeedsothatitcanbeappliedtorealtimesystems.1)CombineHoughTransformandContourAlgorithmforDetectingVLPOurapproachisasfollows:fromtheextractededgingimage,weusethecontouralgorithmtodetectclosedboundariesofobjects.consideredasaplate-candidate.Sincetherearequitefew(black)pixelsinthecontourlines,thetransformationofthesepointstoHoughcoordinaterequiredmuchless第4页computation.Hence,thespeedofthealgorithmisimprovedsignificantlywithoutthelossofaccuracy.However,someplatesmaybecoveredbyglassesordecoratedwithheadlights.Theseobjectsmayalsohavetheshapeoftwointeracted2-parallellines,andtherefore,arealsofalselydetectedasplate-candidates.Torejectsuchincorrectcandidates,weimplementamoduleforevaluatingwhetheracandidateisaplateornot.2)Plate-CandidatesVerificationFromthetwohorizontallinesofacandidate,wecancalculateexactlyhowinclineditwasfromhorizontalcoordinate.Thenweapplyarotatetransformationtoadjustittostraightangle.Afterprocessed,thesestraightbinaryplate-candidateregionswerepassedtoanumberofheuristicsandalgorithmsforevaluating.Ourevaluatingplate-candidatesalgorithmbasesontwomainsteps,whicharetakenrespectively.Thetwostepsare:(a)evaluatetheratiosbetweentheheightsandthewidthsofthecandidates,(b)usehorizontalcrosscutstocountthenumberofcut-objectsinthecandidates.Inthisstage,wecheckandonlyselectoutcandidatesthathavetheratiosofwidthtoheightsatisfyingpre-definedconstraint:minWHRatioW/HmaxWHRatioSincetherearetwomaintypesofVietnameseplates:1-rowand2-row,wehavetwoadequateconstraintsfortwotypes.3.5W/H4.5with1-rowplate-candidates0.8W/H1.4with2-rowplate-candidatesThosecandidateswhichsatisfiedoneofthetwoaboveconstraintsareselectedandpassedtothenextevaluation.EvaluatebyusinghorizontalcrosscutsInthisstage,weusetwohorizontalcutsandthencountthenumberofobjectsthatarecutbythesecrosscuts.Acandidatewillbeconsideredasaplateifthenumberofcutobjectsisinthegivenrangechosensuitablyforeachplatetypebyexperiments.ThisnumbermustbeintheapproximaterangeofthenumberofcharactersinaVLP,wehavetwoappropriateconstraintsfortwotypesofVietnameseplates:PreprocessingOCRSegmentation.ImagestakenVLPDetectionfromcameraLicense-patecharacters:4N8with1-rowplate-candidates7N16with2-rowplate-candidatesWithNisthenumberofcut-objects.Thecandidatesthatsatisfiedoneof第5页thetwoaboveconstraintsareselectedasthefinalresult.Inoursystem,weimplementedtwohoziontalcutsat1/3and2/3ofplate-candidatesheight.Theaverageofnumberofcutobjectswillbecalculated.Thisevaluationhelpstoidentifythecorrectplate-candidates.C.SegmentationTocorrectlyrecognizecharacters,wehavetosegmentabinaryplateimagetosetofimageswhichonlycontainonelicensecharacter.ThesecharacterimageswillbepassedtotheOCRmoduleforrecognizing.Thecommonalgorithmforthistaskisapplyingprojections.However,insomecases,itdoesnotworkcorrectly.Wewillnowdescribeourapproachinsegmentationbyaddingsomeenhancementstothismethod.Weuseahorizontalprojectiontodetectandsegmentrowsin2rowplates.Becausebinaryplateimageswereadjustedtheirinclinedanglestozero,theresultofrowsegmentationisnearlyperfect.Thepositionswithminimumvaluesofhorizontalprojectionarethestartortheendofarowinplate.Differentformrowsegmentation,charactersegmentationismoredifficultduetomanyreasonssuchasstuckcharacters,screws,andmudcoveredinplates.Thesenoisethingscausethecharactersegmentationalgorithmusingverticalprojectiontohavesomemistakes.Insomeworstcasesofbadqualityplateimages,acharactercanbesegmentedintotwopieces.Weapplyseveralconstraintsofratiooftheheighttothewidthofacharacter.Wesearchfortheminimumvaluesintheverticalprojectionandonlytheminimumpositionswhichgivecutpiecessatisfiedallpredefinedconstraintsareconsideredasthepointsforcharactersegmentation.Bythisenhancement,wehaveachievedbetterresultsinthistask.Afterthisstep,wehavealistofcharactercandidates.Notallofthecandidatesareactuallyimagesofcharacters.Bythattime,wecanre-evaluatewhetheraplatecandidateisaplateornotbycheckingthenumberofcharactersofcandidates.InVietnam,aplatecontainsonly7or8characters.Thefinalplatecandidates,togetherwiththeirlistofcharactersarepassedtotheOCRmoduleforrecognizing.D.HiddenMarkovModelforOCRInthissystem,weusetheHMMmodelforcharacterrecognition.The第6页featureswhichweusedinthismodelaretheratioofforegroundpixelsinawindow.Weuseawindowwiththesizeof99,andscanthiswindowintheimagefromlefttorightandtoptobottomThesewindowscanoverlapeachotherbytwothirdsoftheirsize.Bythisway,wehaveafeaturevectorwhichincludes196values.Intherecognitionmodule,weneedtoclassifyacharacterimageintooneof36classes(26alphabetletters:A,B,Cand10numericcharacters:0,1,2).Totrainourmodel,weusetrainingsetswhichwereextractedfromimagesofVLPs.Thenumberofsamplesforeveryclassisabout60.ThesesampleswereextractedfromrealVLPimageswithalittlenoise,soafterwelltrained,themodelcanrecognizeexactlyplateswiththesimilartypesofnoise.Inthelaststep,weusesomespecificrulesofVietnameseVLPstoimproveaccuracy.Welearnedthatthethirdcharacterinplatemustbealetter,thefourthissometimesaletterbutusuallyanumber,andtheotherpositionsaresurelynumbers.IV.EMPIRICALEVALUATIONOursystemwasevaluatedwithtwosetsofVietnamesevehiclesplates.ImagesweretakenbyaSonyDC350digitalcamera,withsizeof800x600pixels,indifferentplacesandtimes.WeuseMicrosoftVisualC+6.0,runonHPWorkstationX2000PentiumIV,1.4GHz,512MBRAM,WindowsXPOS.V.CONCLUSIONSANDFUTUREWORKThesystemperformswellonvarioustypesofVietnameseVLPimages,evenonscratched,scaledplateimages.Inaddition,itcandealwiththecasesofmultipleplatesinthesameimage,ordifferenttypesofvehiclessuchasmotorbikeplates,carplatesortruckplates.However,itstillhasafewerrorswhendealingwithbadqualityplates.Weareworkingonanumberofalgorithmsinthepreprocessingmodule.ThepurposeistodetectregionsthatarelikelyplateregionsfirstandthustoreducethecomputationcostoftheVLPdetectionalgorithm.Inaddition,weintendtocombineanumberoftexture-basedapproach,andmachinelearningmethodstoevaluateplatecadidates.Webelievethesewillimprovetheaccuracyandthespeedofthealgorithmfurthermore.第7页KeyWords:VehicleLicense-PlateRecognition,Real-timeSystem,HoughTransform,ContourAlgorithm.第8页中文翻译稿建立一个自动车辆车牌识别系统摘要车辆由于数量庞大的抽象,现代化的城市要建立有效的交通自动系统管理和调度。最有用的系统之一是车辆车牌（心室晚电位）辨识系统，它能自动捕获车辆图像和阅读这些板块的号码在本文中，我们提出一个自动心室晚电位识别系统，ISeeCarRecognizer，阅读越南样颗粒在交通费的注册号码。我们的系统包括三个主要模块：心室晚电位检测，板数分割和车牌号码识别。在心室晚电位检测模块，我们提出一个有效的边界线为基础Hough变换相结合的方法和轮廓算法。该方法优化速度和准确性处理图像取自不同职位。然后，我们使用水平和垂直投影的车牌号码分开心室晚电位分段模块.最后，每个车牌号码将被OCR的识别模块实现了由隐马尔可夫模型。该系统在两个形象评价实证套并证明其有效性是适用于实际交通收费系统。该系统也可适用于轻微改变一些其他类型的病毒样颗粒。一.导言心室晚电位识别的问题是一个非常有趣，但困难的一个问题.这在许多交通管理系统中是非常有用的.心室晚电位识别需要一些复杂的任务，如心室晚电位检测，分割和承认。这些任务变得更加复杂时，处理各种倾斜角度拍摄的图像或板噪音板的图像。由于此问题通常是在实时系统中使用，它不仅需要准确性，而且要效率。大多数心室晚电位识别应用通过建立减少一些复杂的约束的位置和距离相机车辆，倾斜角度。通过这种方式，心室晚电位识别率识别系统已得到明显改善.在此外，我们可以更准确地获得通过一些具体的当地样颗粒的功能，如字符数，行数在一板，或板的背景颜色或宽度比为一板高.二.相关工作心室晚电位的自动识别问题在20世纪90年代开始就有研究。第一种方法是基于特征的边界线。首次输入图像处理，以丰富的边界线的一些信息如梯度算法过滤器，导致在一边缘图像。这张照片是二值化处理，然后用某些算法，如Hough变换，检测线。最终，2平行线视为板候选人45。另一种方法是基于形态学2。这种方法侧重于一些板块图像性质如亮度，对称，角度等.由于这些特性，这种方法可以检测出图像中的某些相似的性质和找到车牌区域的位置。第三种方法是基于纹理3。在这种方法中，一个心第9页室晚电位被认为是一个对象和不同的纹理帧。大小不同的纹理窗框用于检测板的候选人。每个人获得通过一个分类，以确认它是否是一个盘子或没有。这常用的方法是寻找图像中的文字任务。此外，已经出现了一些其他有关这个问题的方法上注重检测心室晚电位在视频数据。三.拟议的系统我们的系统，ISeeCarRecognizer，由四个模块：前处理，心室晚电位检测，字符分割，和光学字符识别（OCR），在其中最后三个模块处理三个主要问题一个心室晚电位识别域。是VLP检测模块接收到的图像有被处理的预处理模块-第一个输入该系统的模块。这个模块的结果图像发送到分段模块。分割段模块板的图像，成为独立characterimages。这些字符的图像，然后认可光学字符识别模块和最终结果是ASCII字符和板块中的数字。1.预处理从相机拍摄的图像进行处理的预处理模块。本模块的目的是丰富的边缘特征。由于我们的检测方法在边界上的基地功能，它可以改善成功率的心室晚电位检测模块。该算法在此模块顺序使用的老龄化，规范化和直方图均衡。在得到一个灰阶图片中，我们使用过滤器来提取索贝尔边缘图像，然后以一个二进制阈值的一个图像。我们用于局部自适应阈值算法二值化的一步。特别是，我们发展一种基于动态规划，优化其速度，使其适用于实时应用1。图像的结果被用作心室晚电位检测模块的输入。2.心室晚电位检测算法在边界为基础的方法，最重要的步骤是检测边界线。最有效的算法之一是Hough变换申请提取的二进制映像线从对象的图像。然后我们找两平行线，其包含的区域被认为platecandidates。然而，这种方法的缺点是，霍夫变换的执行时间需要太多多的运算时，被应用到一个二进制图像与大量的像素。特别是，较大的图像慢的算法。该算法的速度可能会通过细化图像改进，然后再应用霍夫变换。然而，细化算法也慢。这种限制使这种方法不适合实时交通管理系统。该算法在本系统中我们采用的是组合Hough变换的算法和轮廓产生更高的精度和更快的速度，它可以适用于实时系统。1）结合Hough变换和轮廓算法心室晚电位检测我们的做法是：从提取的边缘图片中，我们使用封闭的轮廓检测算法边界的对象。这些轮廓线改造到霍夫协调，找到两个平行线互动（2-平行线之一成立回另两平行线并建立一个平行四边形表对象）是作为板候选人考虑。由于有相当少（黑）在等高线的像素，转化这些需要协调霍夫点少得多计算。因此，该算法的速度提高没有明显的精度损失。然而，有些板块可能会覆盖眼镜或装饰灯。这些对象还可能有形状两个相互作用二平行线，因此是错误地检测为板候选人。要拒绝这样不正确的候选人，我们评估一个模第10页块的实施无论候选人是板或没有。2）板考生核查从两个候选人的水平线，我们可以如何准确地计算出它从水平倾斜坐标。然后，我们应用旋转转换调整它为平角。经过处理，这些标准二进制板候选区域被传递给一个号码启发式检测和评估算法。我们的评价板候选人在两个算法基地主要步骤，分别采取。这两个步骤是：（1）评价之间的高度和宽度的比例候选人，（2）使用水平横切来计算数切入候选人的对象。我们只选择了检查和候选人有宽度与高度之比满足预先定义约束：minWHRatio宽/高maxWHRatio既然有两种主要类型的越南板：1-行和2行，我们有两个充足两种类型的限制。3.5宽/高4.5一排板候选人0.8宽/高1.4二排板候选人。这些候选人是满足了上述两个一约束选择，并传递到下一个评估。利用水平评价横切。在这个阶段，我们使用两个水平削减和再算上该由这些横切削减对象的数量。一候选人将被视为一个盘子，如果数量的减少选择对象