一种基于卷积神经网络的人群密度识别算法-豆柴文库

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一种基于卷积神经网络的人群密度识别算法
Title:AConvolutionalNeuralNetwork-BasedCrowdDensityRecognitionAlgorithm
Abstract:
Withtherapiddevelopmentofcomputervisiontechnology,crowddensityrecognitionhasbecomeanessentialresearchareawithextensiveapplicationsinvariousfields,includingpublicsafety,crowdcontrol,andurbanplanning.Thispaperpresentsanovelcrowddensityrecognitionalgorithmbasedonconvolutionalneuralnetworks(CNNs).Theproposedalgorithmaimstoaccuratelyestimatecrowddensityfromimagescapturedbysurveillancecamerastoprovideusefulinformationforeffectivecrowdmanagement.
1.Introduction:
Understandingcrowddensityiscrucialforvariousapplicationssuchascrowdmonitoring,eventplanning,andpublicsafety.Traditionalcrowddensityrecognitionmethodsoftenrelyonhumanestimationormanualcounting,whichareerror-proneandtime-consuming.Inrecentyears,CNNshaveshownremarkableperformanceincomputervisiontasks,includingobjectrecognition,segmentation,anddetection.ThispaperproposesaCNN-basedcrowddensityrecognitionalgorithmthatdemonstratessuperioraccuracyandefficiency.
2.RelatedWork:
Thissectionprovidesanoverviewoftheexistingapproachestocrowddensityrecognition.Itreviewstraditionalmethodsbasedonhandcraftedfeaturesandexplainsthelimitationsoftheseapproaches.Subsequently,itdiscussestheapplicationofCNNsincrowdanalysistasksandhighlightstheiradvantagesovertraditionalmethods.
3.DataCollectionandPreprocessing:
Totrainandevaluatetheproposedalgorithm,adatasetofcrowdimagesisrequired.Thissectiondescribesthedatacollectionprocess,includingimageacquisitionthroughsurveillancecameras,annotationofcrowddensity,anddatasetconstruction.Additionally,itexplainsthepreprocessingstepssuchasimageresizing,normalization,andaugmentationtechniquesemployedtoenhancethemodel'srobustness.
4.ConvolutionalNeuralNetworkArchitecture:
TheproposedalgorithmutilizesadeepCNNarchitecturedesignedspecificallyforcrowddensityrecognition.Thissectionpresentsthearchitecture,whichconsistsofconvolutionallayers,poolinglayers,andfullyconnectedlayers.Thechosenarchitectureismotivatedbyitsa