research-article

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

Authors:

Adrian G. Bors,

Hongzhe LiuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 6

Article No.: 216, Pages 1 - 22

https://doi.org/10.1145/3578578

Published: 12 July 2023 Publication History

Abstract

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.

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Index Terms

SSR-Net: A Spatial Structural Relation Network for Vehicle Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
      2. Computer vision representations
        Image representations

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 6

November 2023

858 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3599695

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 12 July 2023

Online AM: 29 December 2022

Accepted: 18 December 2022

Revised: 30 August 2022

Received: 28 February 2022

Published in TOMM Volume 19, Issue 6

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