外文翻译.docx

外文翻译.docx

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外文翻译

（1）原文：

ARobustVision-basedMovingTargetDetectionandTrackingSystem

Abstract

Inthispaperwepresentanewalgorithmforreal~timedetectionandtrackingofmovingtargetsinterrestrialscenesusingamobilecamera.Ouralgorithmconsistsoftwomodes:

detectionandtracking.Inthedetectionmode,backgroundmotionisestimatedandcompensatedusinganaffinetransformation.Theresultantmotionrectifiedimageisusedfordetectionofthetargetlocationusingsplitandmergealgorithm.Wealsocheckedotherfeaturesforprecisedetectionofthetargetlocation.Whenthetargetisidentified,algorithmswitchestothetrackingmode.ModifiedMoravecoperatorisappliedtothetargettoidentifyfeaturepoints.Thefeaturepointsarematchedwithpointsintheregionofinterestinthecurrentframe.Thecorrespondingpointsarefurtherrefinedusingdisparityvectors.Thetrackingsystemiscapableoftargetshaperecoveryandthereforeitcansuccessfullytracktargetswithvaryingdistancefromcameraorwhilethecameraiszooming.Localandregionalcomputationshavemadethealgorithmsuitableforreal-timeapplications.Therefinedpointsdefinethenewpositionofthetargetinthecurrentframe.Experimentalresultshave

shownthatthealgorithmisreliableandcansuccessfullydetectandtracktargetsinmostcases.

Keywords:

realtimemovingtargettrackinganddetection,featurematching,affinetransformation,vehicletracking,mobilecameraimage.

1Introduction

Visualdetectionandtrackingisoneofthemostchallengingissuesincomputervision.Applicationofthevisualdetectionandtrackingarenumerousandtheyspanawiderangeofapplicationsincludingsurveillancesystem,vehicletrackingandaerospaceapplication,tonameafew.Detectionandtrackingofabstracttargets（e.g.vehiclesingeneral）isaverycomplexproblemanddemandssophisticatedsolutionsusingconventionalpatternrecognitionandmotionestimationmethods.Motion-basedsegmentationisoneofthepowerfultoolsfordetectionandtrackingofmovingtargets.Itissimpletodetectmovingobjectsinimagesequencesobtainedbystationarycamera[1],[2],theconventionaldifference-basedmethodsfailtodetectmovingtargetswhenthecameraisalsomoving.Inthecaseofmobilecameraalloftheobjectsintheimagesequencehaveanapparentmotion,whichisrelatedtothecameramotion.Anumberofmethodshavebeenproposedfordetectionofthemovingtargetsinmobilecameraincludingdirectcameramotionparametersestimation[3],opticalflow[4],[5],andgeometrictransformation[6],[7].Directmeasurementofcameramotionparametersisthebestmethodforcancellationoftheapparentbackgroundmotionbutinsomeapplicationitisnotpossibletomeasuretheseparametersdirectly.Geometrictransformationmethodshavelowcomputationcostandaresuitableforrealtimepurpose.Inthesemethods,auniformbackgroundmotionisassumed.Anaffinemotionmodelcouldbeusedtomodelthismotion.Whentheapparentmotionofthebackgroundisestimated,itcanbeexploitedtolocatemovingobjects.Inthispaperweproposeanewmethodfordetectionandtrackingofmovingtargetsusingamobilemonocularcamera.Ouralgorithmhastwomodes:

detectionandtracking.Thispaperisorganizedasfollows.InSection2,thedetectionprocedureisdiscussed.Section3describesthetrackingmethod.ExperimentalresultsareshowninSection4andconclusionappearsinSection5.

2Targetdetection

InthedetectionmodeweusedaffinetransformationandLMedS（Leastmediansquared）methodforrobustestimationoftheapparentbackgroundmotion.Afterthecompensationofthebackgroundmotion,weapplysplitandmergealgorithmtothedifferenceofcurrentframeandthetransformedpreviousframetoobtainanestimationofthetargetpositions.Ifnotargetisfound,thenitmeanseitherthereisnomovingtargetinthesceneor,therelativemotionofthetargetistoosmalltobedetected.Inthelattercase,itispossibletodetectthetargetbyadjustingtheframerateofthecamera.Thealgorithmaccomplishesthisautomaticallybyanalyzingtheproceedingframesuntilamajordifferenceisdetected.Wedesignedavotingmethodtoverifythetargetsbasedonaprioriknowledgeofthetargets.Forthecaseofvehicledetectionweusedverticalandhorizontalgradientstolocateinterestingfeaturesaswellasconstraintonareaofthetargetasdiscussedinthissection.

2.1Backgroundmotionestimation

Affinetransformation[8]hasbeenusedtomodelmotionofthecamera.Thismodelincludesrotation,scalingandtranslation.2~Daffinetransformationisdescribedasfollow:

（1）

where（xi,yi）arelocationsofpointsinthepreviousframeand（Xi,Yi）arelocationsofpointsinthecurrentframeanda1~a6aremotionparameters.Thistransformationhassixparameters;therefore,threematchingpairsarerequiredtofullyrecoverthemotion.Itisnecessarytoselectthethreepointsfromthestationaryback~groundtoassureanaccuratemodelforcameramotion.WeusedMoravecoperator[9]tofinddistinguishedfeaturepointstoensureprecisematch.Moravecoperatorselectspixelswiththemaximumdirectionalgradientinthemin~maxsense.

Ifthemovingtargetsconstituteasmallarea（i.e.lessthan50%）oftheimage,thenLMedSalgorithmcanbeappliedtodeterminetheaffinetransformationparametersoftheapparentbackgroundmotionbetweentwoconsecutiveframesaccordingtothefollowingprocedure.

1.SelectNrandomfeaturepointfrompreviousframe,andusethestandardnormalizedcrosscorrelationmethodtolocatethecorrespondingpointsinthecurrentframe.Normalizedcorrelationequationisgivenby:

（2）

here

and

aretheaverageintensitiesofthepixelsinthetworegionsbeingcompared,andthesummationsarecarriedoutoverallpixelswithinsmallwindowscenteredonthefeaturepoints.Thevaluerintheaboveequationmeasuresthesimilaritybetweentworegionsandisbetween1and-1.Sinceitisassumedthatmovingobjectsarelessthan50%ofthewholeimage,thereforemostoftheNpointswillbelongtothestationarybackground.

2.SelectMrandomsetsofthreefeaturepoints:

（xi,yi,Xi,Yi）fori=1,2,3,fromtheNfeaturepointsobtainedinstep1.（xi,yi）arecoordinatesofthefeaturepointsinthepreviousframe,and（Xi,Yi）aretheircorrespondsincurrentframe.

3.Foreachsetcalculatetheaffinetransformationparameters.

4.TransformNfeaturepointsinstep1usingMaffinetransformations,obtainedinstep3andcalculatetheMmediansofsquareddifferencesbetweencorrespondingpointsandtransformedpoints.Thenselecttheaffineparametersforwhichthemedianofsquareddifferenceistheminimum.

Accordingtotheaboveprocedure,theprobabilitypthatatleastonedatasetinthebackgroundandtheircorrectcorrespondingpointsareobtainedisderivedfromthefollowingequation[7]:

（3）

where

（<0.5）istheratioofthemovingobjectregionstowholeimageandqistheprobabilitythatcorrespondingpointsarecorrectlyfind.In[7]ithasbeenshownthattheabovemethodwillgiveanaccurateandreliablemodel.

2.2Movingtargetdetectionusingbackgroundmotioncompensatedframes

Whenaffineparametersareestimated,theycanbeusedforcancellationoftheapparentbackgroundmotion,bytransformationofpreviousframe.Nowdifferenceofthecurrentframeandtransformedpreviousframerevealstruemovingtargets.Thenweapplyathresholdtoproduceabinaryimage.Theresultsofthetransformationandsegmentationareshownisfigure1~aand1~b.Somepartsaresegmentedasmovingtargetsduetonoise.Connectedcomponentpropertycanbeappliedtoreduceerrorsduetonoise.Weusesplitandmergealgorithmtofindtargetbounding-boxes.Ifnotargetisfound,thenitmeanseitherthereisnomovingtargetinthesceneor,therelativemotionofthetargetistoosmalltobedetected.Inthelattercase,itispossibletodetectthetargetbyadjustingtheframerateofthecamera.Thealgorithmaccomplishesthisautomaticallybyanalyzingtheproceedingframesuntilatargetisdetected.Ourspecialinterestisdetectionandtrackingofthemovingvehiclessoweusedaspectratioandhorizontalandverticallineasconstraintstoverifyvehicles.Ourexperimentsshowthatcomparisonofthelengthofhorizontalandverticallinesinthetargetareawiththeperimeterofthetargetwillgiveagoodclueaboutthenatureofthetarget.

3Targettracking

Afteratargetisverified,thealgorithmswitchesintothetrackingmode.ModifiedMoravecoperatorisappliedtothetargettoidentifyfeaturepoints.Thesefeaturepointsarematchedwithpointsintheregionofinterestinthecurrentframe.Disparityvectorsarecomputedforthematchedpairsofpoints.Weuseddisparityvectorstorefinethematchedpoints.Therefinedpointsdefinethenewpositionofthetargetinthecurrentframe.Thealgorithmswitchestothedetectionmodewheneverthetargetismissed.Althoughthedetectionalgorithmdescribedabovecanbeusedfortrackingtoobutthetrackingalgorithm,wedescribeinthissectionhasverylowcomputationcostincontrastwiththedetectionalgorithmdescribedabove.Ontheotherhandwhenthetargetisdetecteditisnotrestrictedtokeepmovingintrackingmode.Thetargetcanalsobelargerthan50%ofthesceneryinthetrackingmodeandthismeanscameracanzoomtohavealargerviewofthetargetwhiletracking.

Figure1:

twoconsecutiveframesanddifferenceofthemafterbackgroundmotioncompensation,thecalculatedaffineparametersare:

a1=0.9973,a2=-0.004,a3=0.008,a4=1.0022,a5=1.23,a6=-2.51

Whenthesizeofthetargetisfixedtheno