文献类型：期刊文献

英文题名：SSR-Net: A Spatial Structural Relation Network for Vehicle Re-identification

作者：Xu, Zheming[1];Wei, Lili[1];Lang, Congyan[1];Feng, Songhe[1];Wang, Tao[1];Bors, Adrian G.[2];Liu, Hongzhe[3]

第一作者：Xu, Zheming

通讯作者：Lang, CY[1]

机构：[1]Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, 3 Shangyuancun, Beijing 100044, Peoples R China;[2]Univ York, York, England;[3]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China

第一机构：Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, 3 Shangyuancun, Beijing 100044, Peoples R China

通讯机构：[1]corresponding author), Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, 3 Shangyuancun, Beijing 100044, Peoples R China.

年份：2023

卷号：19

期号：6

外文期刊名：ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS

收录：;EI(收录号：20233414595178);Scopus(收录号：2-s2.0-85168234630);WOS:【SCI-EXPANDED(收录号:WOS:001035785200038)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant nos. 62072027, 61872032, and 62076021.

语种：英文

外文关键词：Vehicle re-identification; Graph Convolution Network; attention mechanism; deep learning

摘要：Vehicle re-identification (Re-ID) represents the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various feature learning-based approaches merely focusing on feature representations including global features or local features to obtain more subtle details to identify highly similar vehicles. However, few such methods consider the spatial geometrical structure relationship among local regions or between the global and local regions. By contrast, in this study, we propose a Spatial Structural Relation Network (SSR-Net) that explores the above-mentioned two kinds of relations simultaneously to learn more discriminative features by modeling the spatial structure information and global context information. In this article, we propose to adopt a Graph Convolution Network (GCN), for modeling spatial structural relationships among characteristic features. The GCN model aggregating the local and global features is shown to be more discriminative and robust to several car image transformations. To improve the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.