Vehicle Re-identification Based on Information Mining in Low Active Region
SUN Yajie
SUN Yingying
CAO Xiaoling
YANG Jian
Abstract：Vehicle re-identification(Re-ID)is designed to identify the same vehicle with multiple non-overlapping cameras.The existing re-identification studies mainly extract vehicle features in the high information area of the image,but ignore that there may be important information conducive to vehicle re-identification in the low activity area.To solve this problem,this paper propos-es a low active region information mining for vehicle re-identification,LRM-Net,by erasing the high active region,mining the key features in the low active region to enhance the performance of the network.LRM-Net has two branch structures,which are global branch and feature masking branch module.The global branch is used to learn the overall appearance characteristics of the vehicle.Feature masking module is used to learn the distinguishing vehicle features in the low active area.When the vehicle features are very similar,this module can distinguish vehicles effectively.Using two common data sets of VehicleID(large)and VeRi-776,the Rank-1 accuracy of LRM-Net is 90.7%and 95.9%,and the mean accuracy(mAP)is 77.1%and 79.7%,respectively.Qualitative and quantitative experiments show the effectiveness of LRM-Net in mining information in low active regions.
Keywords：vehicle re-identificationlow active regionfeature masking module
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3264-3269 )