外文翻译-视频监控系统

ASystemforVideoSurveillanceand

Monitoring

ThethrustofCMUresearchundertheDARPAVideoSurveillanceand

Monitoring(VSAM)projectiscooperativemulti-sensorsurveillancetosupport

battlefieldawareness.UnderourVSAMIntegratedFeasibilityDemonstration(IFD)

contract,wehavedevelopedautomatedvideounderstandingtechnologythatenablesa

singlehumanoperatortomonitoractivitiesoveracomplexareausingadistributed

networkofactivevideosensors.Thegoalistoautomaticallycollectanddisseminate

real-timeinformationfromthebattlefieldtoimprovethesituationalawarenessof

commandersandstaff.Othermilitaryandfederallawenforcementapplications

includeprovidingperimetersecurityfortroops,monitoringpeacetreatiesorrefugee

movementsfromunmannedairvehicles,providingsecurityforembassiesorairports,

andstakingoutsuspecteddrugorterroristhide-outsbycollectingtime-stamped

picturesofeveryoneenteringandexitingthebuilding.

Automatedvideosurveillanceisanimportantresearchareainihecommercial

sectoraswell.Technologyhasreachedastagewheremountingcamerastocapture

videoimageryischeap,butfindingavailablehumanresourcestositandwatchthat

imageryisexpensive.Surveillancecamerasarealreadyprevalentincommercial

establishments,withcameraoutputbeingrecordedtotapesthatareeitherrewritten

periodicallyoistoredinvideoarchives.Afteracrimeoccurs-aslorcisrobbeduia

carisstolen-investigatorscangobackafterthefacttoseewhathappened,butof

coursebythenitistoolate.Whatisneedediscontinuous24-hourmonitoringand

analysisofvideosurveillancedatatoalertsecurityofficerstoaburglaryinprogress,

ortoasuspiciousindividualloiteringintheparkinglot,whileoptionsarestillopen

foravoidingthecrime.

Keepingtrackofpeople,vehicles,andtheirinteractionsinanurbanorbattlefield

environmentisadifficulttask.TheroleofVSAMvideounderstandingtechnologyin

achievingthisgoalistoautomatically“parse“peopleandvehiclesfromrawvideo,

determinetheirgeolocations,andinsertthemintodynamicscenevisualization.We

havedevelopedrobustroutinesfordetectingandtrackingmovingobjects.Detected

objectsareclassifiedintosemanticcategoriessuchashuman,humangroup,car.and

truckusingshapeandcoloranalysis,andtheselabelsareusedtoimprovetracking

usingtemporalconsistencyconstraints.Furtherclassificationofhumanactivity,such

aswalkingandrunning,hasalsobeenachieved.Geolocationsoflabeledentitiesare

determinedfromtheirimagecoordinatesusingeitherwide-baselinestereofromtwo

ormoreoverlappingcameraviews,orintersectionofviewingrayswithaterrain

modelfrommonocularviews.Thesecomputedlocationsfeedintoahigherlevel

trackingmodulethattasksmultiplesensorswithvariablepan,tiltandzoomto

cooperativelyandcontinuouslytrackanobjectthroughthescene.Allresultingobject

hypothesesfromallsensorsaretransmittedassymbolicdatapacketsbacktoacentral

operatorcontrolunit,wheretheyaredisplayedonagraphicaluserinterfacetogivea

broadoverviewofsceneactivities.Thesetechnologieshavebeendemonstrated

throughaseriesofyearlydemos,usingatestbedsystemdevelopedontheurban

campusofCMU.

Detectionofmovingobjectsinvideostreamsisknowntobeasignificant,and

difficult,researchproblem.Asidefromtheintrinsicusefulnessofbeingableto

segmentvideostreamsintomovingandbackgroundcomponents,detectingmoving

blobsprovidesafocusofattentionforrecognition,classification,andactivityanalysis,

makingtheselaterprocessesmoreefficientsinceonly“moving“pixelsneedbe

considered.

Therearcthreeconventionalapproachestomovingobjectdetection:temporal

differencing;backgroundsubtraction;andopticalflow.Temporaldifferencingisvery

adaptivetodynamicenvironments,butgenerallydoesapoorjobofextractingall

relevantfcaluicpixels.Backgroundsublraulioiiprovidesdiemostcompletefcaluic

data,butisextremelysensitivetodynamicscenechangesduetolightingand

extraneousevents.Opticalflowcanbeusedtodetectindependentlymovingobjectsin

thepresenceofcameramotion;however,mostopticalflowcomputationmethodsare

computationallycomplex,andcannotbeappliedtofull-framevideostreamsin

real-timewithoutspecializedhardware.

UndertheVSAMprogram,CMUhasdevelopedandimplementedthreemethods

formovingobjectdetectionontheVSAMtestbed.Thefirstisacombinationof

adaptivebackgroundsubtractionandthree-framedifferencing.Thishybridalgorithm

isveryfast,andsurprisinglyeffective-indeed,itistheprimaryalgorithmusedbythe

majorityoftheSPUsintheVSAMsystem.Inaddition,twonewprototypealgorithms

havebeendevelopedtoaddressshortcomingsofthisstandardapproach.First,a

mechanismformaintainingtemporalobjectlayersisdevelopedtoallowgreater

disambiguationofmovingobjectsthatstopforawhile,areoccludedbyotherobjects,

andthatthenresumemotion.Onelimitationthataffectsboththismethodandthe

standardalgorithmisthattheyonlyworkfbrstaticcameras,orina“stepandstare^

modeforpan-tiltcameras.Toovercomethislimitation,asecondextensionhas

beendevelopedtoallowbackgroundsubtractionfromacontinuouslypanningand

tiltingcamera.Throughcleveraccumulationofimageevidence,thisalgorithmcanbe

implementedinreal-timeonaconventionalPCplatfonn.Afourthapproachto

movingobjectdetectionfromamovingairborneplatformhasalsobeendeveloped,

underasubcontracttotheSarnoffCorporation.Thisapproachisbasedonimage

stabilizationusingspecialvideoprocessinghardware.

ThecurrentVSAMIFDtestbedsystemandsuiteofvideounderstanding

technologiesaretheendresultofathree-year,evolutionaryprocess.Impetusforthis

evolutionwasprovidedbyaseriesofyearlydemonstrations.Thefollowingtables

provideasuccinctsynopsisoftheprogressmadeduringthelastthreeyearsinthe

areasofvideounderstandingtechnology,VSAMtestbedarchitecture,sensorcontrol

algorithms,anddegreeofuserinteraction.Althoughtheprogramisovernow,the

VSAMIFDtestbedcontinuestoprovideavaluableresourceforthedevelopmentand

testingofnewvideounderstandingcapabilities.Futureworkwillbedirectedtowards

achievingthefollowinggoals:

1betterunderstandingofhumanmotion,includingsegmentationandtrackingof

articulatedbodyparts;

1improveddataluggingandicliicvalmcclianisnistosupport24/7syslcm

operations;

1bootstrappingfunctionalsitemodelsthroughpassiveobservationofscene

activities;

1betterdetectionandclassificationofmulti-agenteventsandactivities;

1bettercameracontroltoenablesmoothobjecttrackingathighzoom;and

1acquisitionandselectionof"bestviews“withtheeventualgoalofrecognizing

individualsinthescene.

视频监控系统

和不相干的活动。光流可以用来检测独立移动的物体，在场的摄像机运动，但大

多数的光流计算方法的计算复杂，不能适用于全帧视频流的实时没有专门的硬

件。

根据VSAM计划，债务工具中央结算系统制定并实施了三种方法的运动目

标检测的VSAM试验，首先是结合自适应背景减除与三帧差分。这种混合算法

是非常快，令人惊