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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
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