多尺度降噪自編碼器的遮擋行人重識別研究與應(yīng)用
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中圖分類號：TP391 文獻標志碼：A 文章編號：1001-3695（2025）07-040-2220-07
doi：10.19734/j.issn.1001-3695.2024.09.0372
Abstract：Toaddress theisueofocclusioninpersonre-identification（ReID）andalleviate theimpactof insufficientocclusion datasets，this research focusedonoccudedReIand proposedamultiscaledenoising autoencoder-based method.The method usedknowledgedistllationlearninginastudent-teachermodelforjointtraining，enablingthetransferofknowledgefromthe teachermodel tothestudentmodel.Usingartificiallyccludedimages totraintheautoencoder，compressedtheinputdatainto alatentspacefeaturerepresentation，decoded toreconstructdatathatcloselyresemblestheoriginalinput，achievingdenoising reconstruction.Basedonthetrainedautoencoder，further trainingwithealocludedimagesandincorporatinganatentionmo duletodiferentiatebetweenthefeaturerepresentationsofoludedimagesandholisticimages，enhancedthemodel’srobustnesand recognition performance foroccluded images.Experimentsdemonstrate thattheproposed methodachievessuperior performance onthe Occude-Duke，Occluded-ReID，and Partial-ReIDdatasets compared tocurrentlyadvancedoccluded pedestrianre-identification approaches.
Key words：person re-identification；occlusion；denoising autoencoder；knowledge distillation
0 引言
行人重識別（ReID）是計算機視覺領(lǐng)域的一個重要研究方向，旨在解決如何在不同監(jiān)控攝像頭中識別同一行人的問題。（剩余21145字）