Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking  

作　　者：Qi WANG Weidong MIN Daojing HE Song ZOU Tiemei HUANG Yu ZHANG Ruikang LIU 

机构地区：[1]School of Information Engineering,Nanchang University,Nanchang 330031,China [2]School of Software,Nanchang University,Nanchang 330047,China [3]Jiangxi Key Laboratory of Smart City,Nanchang University,Nanchang 330047,China [4]School of Computer Science and Software Engineering,East China Normal University,Shanghai 200062,China

出　　处：《Science China(Information Sciences)》2020年第11期139-150,共12页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61762061,62076117);National Key R&D Program of China(Grant Nos.2017YFB0801701,2017YFB0802805);Natural Science Foundation of Jiangxi Province(Grant No.20161ACB20004);Jiangxi Key Laboratory of Smart City(Grant No.20192BCD40002)。

摘　　要：Research on the application of vehicle re-identification to video surveillance has attracted increasingly growing attention.Existing methods are associated with the difficulties of distinguishing different instances of the same car model owing to the incapability of recognizing subtle differences among these instances and the possibility that a subtle difference may lead to incorrect results of ranking.In this paper,a discriminative fine-grained network for vehicle re-identification based on a two-stage re-ranking framework is proposed to address these issues.This discriminative fine-grained network(DFN)is composed of fine-grained and Siamese networks.The proposed hybrid network can extract discriminative features of the vehicle instances with subtle differences.The Siamese network is rather suitable for general object re-identification using two streams of the network,while the fine-grained network is capable of detecting subtle differences.The proposed two-stage re-ranking method allows obtaining a more reliable ranking list by using the Jaccard metric and merging the first and second re-ranking lists,where the latter list is formed using the sample mean feature.Experimental results on the VeR i-776 and VehicleID datasets show that the proposed method achieves the superior performance compared to the state-of-the-art methods used in vehicle re-identification.

关 键 词：vehicle re-identification DFN two-stage re-ranking FINE-GRAINED Jaccard metric 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP391.41[交通运输工程—道路与铁道工程]