A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

作     者：Wei Sun Xuan Chen Xiaorui Zhang Guangzhao Dai Pengshuai Chang Xiaozheng He 

作者机构：Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing210044China School of AutomationNanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital ForensicsMinistry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic InstituteTroyNY12180USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3549-3561页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219 in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401 in part,by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant Numbers SJCX21_0363 in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund 

主　　题：Vehicle re-identification region batch dropblock multi-feature learning local attention 

摘      要：Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera *** has gradually become a core technology of intelligent transportation *** existing vehicle re-identification models adopt the joint learning of global and local ***,they directly use the extracted global features,resulting in insufficient feature ***,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional *** solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this *** model consists of global and local *** global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation *** addition,multi-scale pooling operations are used to obtain multiscale *** the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local *** features from both branches are combined to provide a more comprehensive and distinctive feature *** experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.