ContentOrientedSurveillanceSystemBasedonInformation-CentricNetwork

XinQiWaseda Univers i ty, Tokyo

samqxxx@akane.waseda. jp

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OutlineIntroduction

Architecture◦ SystemArchitecture◦ ApplicationArchitecture◦ ContentNamingStrategy

FieldExperiment

Evaluation

Conclusion

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IntroductionUrban surveillance systems are being applied in a rapid pace with mature butinefficient solutions. The inefficiency is revealed with two aspects:

ØToo concentrated bandwidth consumption

ØToo concentrated processing requirement

To solve this problem, we proposed a content oriented surveillance system basedon Information- Centric Network. Instead of streaming live video to the centraldata center and processing multiple data stream in the same time, we havedesigned the nodes to process the captured raw data and produce objectivecontents for the subscriber.

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NetworkArchitectureThisfigure showsthenetworkarchitectureofthemethod.ThecameranodeswereconnectedviaICNnetworkanddividedintoseveralsetscoveringcertainareas.Insideeachsettherewasanodewithcontrolfunction,thisnodecouldrespondtoacomplexrequestbymanipulatingothernodesintheset.

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NetworkArchitectureThefigureshowstheflowchartofafullrequestingprocedure.Theprocedurestartedfromthegenerationoftheinterest.Theinterestwasacombinationoftheobjective’spatternparameterandtheareacodeforrecognition.Aftersendingouttheinteresttothecameranodescoveringtargetarea.Thecameranodeswouldinstantlystarttoanalyzethefreshcapturedframewiththegivenpatternparameter,thengeneratedtheresultdatatobetransmittedback.

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ApplicationArchitectureTheuser,forexample,couldinstructtheconsumerapplicationtosendarequestforthehumancountinareaone,containing4nodes.Aninterestwasgeneratedbytheapplicationandsentout.Oncetheinteresthitamatchingcontrolnode,thecontrolnodewouldinstructallthenodesincontrol(includingitself)togatherandintegrateinformationwithdifferentinterestcombination,thensentthecontentback.

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ApplicationArchitectureWeappliedtwokindsofparameterstothepatternrecognitionapplicationusedintheexperiment.Theywerefacecounterandcloth-colorrecognitionbasedonopensourcesoftware,OpenCV.Duringtheanalyzeoftheapplication,firstitlocatedthefaceareasofpossiblehumanappearancesinthecapturedframe.Whilecountingthevalueofthefacenumber,itlocatedtheupperbodyclothareasrightbeloweveryfacearea.Bysummarizingtheareapixels’ colorvalue,outputthefacecountandcloth-colordatatogetherorseparately.Algorism1describesthepatternrecognitionprocedures.

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ApplicationArchitectureOntherightshowstheexpressionoftherecognitionareasappliedonaperson.Theredzonecoversthefacearea,andbelowthatisthebluezone,coveringupperbody’sclotharea.First,therecognitionapplicationcheckstheexecutionparametertomakesureitislegal.Thenfindsoutifitdesiresfacecountingandcloth-colorrecognizing.Iftheapplicationispermittedtoexecutethefacerecognitionfunction,itwouldquicklyrecognizeeveryfaceintheframeandoutputthecountvalue.Afterthecounting,theapplicationwouldcheckifthereisacloth-colorrecognitioncommand.Ifthereisone,theapplicationwouldlocatetheareaundereveryfaceandsummarizethecolordataandthenoutputthecolordata.Withtheoutputtingofthedesireddata,thesystemcouldreplythedesiredcontent.

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ContentNamingStrategyWeusedCCNx ,torealizetheICNnetworkarchitecture.AccordingtotheprincipleofCCNxfunctionalities,theservingapplicationmustcontainacertainformatofnameprefix.Hereweconsiderthenameprefixastheidentifierofthedesiredcontent.Inordertoapplydifferentparameterstothepatternsrecognitionappliance,theparameterswerefittedintothenameprefixinacertainformat.Thisfigureshowsanexampleofthefullnameprefixinthemethod.

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ContentNamingStrategyInordertomanagethelargescaleofthecameranodes,apropercontentnamecombinationisorganized.Thesystemappliesnamestothecontentsandclassifiesthecontentsinthesametime,makingitefficientsortinghugeamountofcontentsprovidedbythenodes,especiallytohumanusers.Forexample,theusercouldeasilyinstructthespecifictargetcameranodebyfollowingthesystemclassmapinthefigure.

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FieldExperimentWehadperformedafieldexperimentinMarch,atMojiPort(Mojiko),Kyushu,whichusedtobeaninternationaltradingportsincethelate19thcentury.

Inordertofulfillthepurposeofthedesignedarchitecture.Everycameranodewasgivencertainlevelofprocessingpower.

Therewere20cameranodesintheexperiment,dividedinto5setsevenlyandsignedto5experimentlocationsreferringontheright.Werentedoneofthefacilities’roomastheofficethatoperatedourexperiment.1. Kanmon KaikyoMuseum2. OldDalianLineShed(Rentedasoffice)

3. Mojiko RetroObservationTower1F4. MojiCustomsBuilding5. OldOsakaMerchantShipBuilding

Powersupply DC5V/2A

Hardware IntelComputeStickSTCK1A8LFC

Capturedevice UVCwebcam

Networkdevice(sharedby4units) LTEmobileWi-Fiaccesspoint

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EvaluationThesefiguresshowtheexperimentdataofthe5locations.Thefiguresrepresentedthequantityandproportionofthecoloroftheareas,meanwhilethecoloredbarsrepresentedthehumancount.

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EvaluationThereweretwomajorconclusionsfromtheexperiment’sresultsbasedontherepresentationofthehumancountingandcloth-color.Firstwecouldeasilyfindoutthetourist’sdensitywasmainlyconcentratedinafternoontimeexceptlocation2,OldDalianLineShed.Thismightbecausethereweremanypeoplerentedthefacility’sroomsonthatday,includingourexperimentingteam.Ontheotherhand,thereappearedtohavemostofthecolorsingrey,blackandotherdarkcolors.Theresultstandsfortheappearancethatbigpartofthewinterclothchoicewasdarkcolorslikegreyandblack,alongwithotherbrightcolorsinasmallpercentage.

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EvaluationFortheevaluationofourproposedmethod,weanalyzedthethroughputfromlocation3.Figureshowsthethroughputcomparing.Fromthefigurewecansee2throughputlines,blueandred.Thebluelinerepresentsthedatarateofatypicalsurveillancesystemwhichusesvideostreamingastheirdatafeed.Theredlinerepresentsthedataratefromthemethodweproposed,thedataisgeneratedonlywhenthetargetcontentisbeingretrieved.Foratypicalhigh-definitionvideofeed,thenetworkbandwidthconsumptionisaround4Mbps.Fromthethroughputwecantellthat,inacontinuestime,ourproposedmethodconsumeslessnetworkbandwidththanthetraditionalsurveillancesystem.Thisisbecausetheonlycontentsbeingtransferredinthenetworkarethosematchesthetarget’sinterests.Whenthesystemisinidle,thewholenetworkconsumptiondropstoalmostzero.

Thecomparedresultshowsgreatnetworkbandwidthsaveinthefield.Utilizingthisbenefit,thesystemcouldperformveryvaluableinareaswithlownetworkbandwidth,alsoinsomedisasterscenarios.Meanwhilethepatternrecognitionappliancescouldworkinanti-terrorismscenarios.

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ConclusionAcontentorientedsurveillancesystemisproposedinthispaperforthepurposeofutilizingnamedcontentsinmodernsurveillancesystems.WiththeICNarchitectureoptimizingthesystemperformanceundermultipleclientssubscribing.Thismethodoptimizesthenetworkbandwidthusageandefficientlydistributesprocessingpowerneedstothenodes.Whenthereareenoughnodescoveringmonitoringareas,itisalsopossibletopredicttarget’smovingpatterntoforeseethenextappearinglocation.Thismethodcouldperformgreatlyinurbanscenariossuchasflowcontrolinpopularareasandanti-terrorismbyevaluatingpeople’sappearances.

Withthefoundationofourcontentorientednetworkingarchitecture,moreappliances,likemotiondetectingandsoundanalyzing,couldbedevelopedtoutilizetheICNnetwork.Seeingthefutureofbigdataanalyzingandprocessing,webelievethedistributedcomputingsolutioncouldalsobenefitinadvance.Meanwhiletheproposedmethodhasalreadyopenedupasolutionofapplyingsurveillancesystemoverlowbandwidthnetworkindisasterscenarios,anddistributingtargetacquirementinanti-terrorismscenarios.

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Q&A

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