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RealTimeMonitoringofCCTVCameraImagesUsingObjectDetectorsandSceneClassificationforRetailandSurveillance
Applications– AnandJoshi([email protected])
IntroductionCurrentsurveillanceandcontrolsystemsinretailandelsewhere,stillrequirehumansupervisionandintervention.ThisworkwilltrytoprovideadetectionsysteminCCTVvideosonrealtimebasis,appropriatefor;surveillanceandcontrol,inventorytracking,theftdeterrence,threatperceptionanddetectionetc.andapplyMachineLearning/DeepLearningtechniquesforrealworldapplications.Thiswilltrytoautomatemanytasks,whichcanbeerrorproneotherwiseduetohumanerrorsandfatigue.Thissolutioncanpotentiallyhavecapabilitytoproviderealtimealerts,notificationonsmartphones/tabletsandproviderichdataforanalyticspurpose.
DatasetTheDatasetComprisedofColorimagesinfollowingcategories:a)EveryDayObjects foundinretailenvironment,obtainedfromImageNet.Over1.2millionimagesusedfortraining,dividedinover1000classes.b)GunsandKnives:KnivesImagesDatabase,whichcontains9340negativeexamplesand3559positiveexamples,InternetMovieFirearmsDatabase,whichcontains8557imagesc)HumanHand:HandDataset whichcontainsabout14700handimagesfromvarioussources.EgoHands Dataset containingabout120000images.90% wasusedfortraining and10% wasusedforvalidation.
SomeImageSamples
ApplicationFlowandSystemSetup
DetectionusingCNN
Matchfound? yes
SendPushNotificationreq usingNODE.js
ApplePushNotificationService
FeatureExtractionandPredictionusingCNN
ConvolutionLayer:asetoflearnablefilters(kernels).Representsaspecificpartoftheimagebypreservingthespatialrelationshipbetweenpixels.Poolinglayer(subsampling):reducesthedimensionalityofeachfeaturemapbutretainsthemostimportantinformation.Fullyconnectedlayerinvolvesasoftmax functionwhichwillhelpusmaketheprediction,byexponentiationandthennormalizingtheinputs.Itsoutputrepresenttheprobabilities(confidence)ofeachclassprediction.𝒔𝒐𝒇𝒕𝒎𝒂𝒙(𝒙)𝒊 = 𝒆𝒙𝒑(𝒙𝒊)
𝚺𝒋𝒆𝒙𝒑(𝒙𝒊)
ThisismostappropriateforImageclassificationproblem
CNNArchitecture:Inception-Resnet V2.Itismoreaccuratethanpreviousstateoftheartmodels.TheTop-1andTop-5validationaccuraciesonthe ILSVRC2012imageclassificationbenchmark basedonasinglecropoftheimageis80.4&95.3respectively.DeepLearningFramework– TensorFlow,fornumericalcomputationusingdataflowgraphs.Nodesinthegraphrepresentmathematicaloperations,whilethegraphedgesrepresentthemultidimensionaldataarrays(tensors)communicatedbetweenthem.
TrainingEpochs SolverType BaseLearningRate100 StochasticGradient
Descent0.0001
#Epoch Solver LearningRate
Accuracy
100 SGD 0.0001 99.97
REFERENCES[1] A.Krizhevsky,I.Sutskever,andG.E.Hinton,“Imagenet classificationwithdeepconvolutionalneuralnetworks,”inAdvancesinneuralinformationprocessingsystems,pp.1097–1105,2012.[2]ScalableObjectDetectionusingDeepNeuralNetworksDumitru Erhan,ChristianSzegedy,AlexanderToshev,andDragomir Anguelov Google,Inc.[3]AutomaticHandgunDetectionAlarminVideosUsingDeepLearningRobertoOlmos,Siham Tabik,andFranciscoHerrera[4]CCTVobjectdetectionwithfuzzyclassificationandimageenhancement,AndrzejMATIOLAŃSKI,AleksandraMAKSIMOWA,AndrzejDZIECH,MultimediaToolsandApplications,2015[5]AutomatedDetectionofFirearmsandKnivesinaCCTVImage,Michał Grega,AndrzejMATIOLAŃSKI,PiotrGuzik,Mikołaj Leszczuk,Sensors,ISSN1424-8220
FutureDespiteInception-ResNetV2performingthebest,I foundthatmanypredictionshadaprobabilityof20%to40%,evenifthesepredictionswerecorrect.ThefirststepI wouldliketotakeistoincreasetheconfidenceinthesepredictionssothatthemodelwouldbemorewelltrained.ThiscouldbedonemytrainingitonmoredataorincreasingtheepochswhentrainingtheCNN.Alsoafterdevelopinganend-to-endProofOfConceptsolution,Istronglyfeelthatithasthepotentialofbecomingacommerciallyviableproduct
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