Open Access

ARTICLE

AG-GCN: Vehicle Re-Identification Based on Attention-Guided Graph Convolutional Network

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

1 School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 College of Information Engineering, Taizhou University, Taizhou, 225300, China

* Corresponding Author: Sai Ji. Email:

(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)

Computers, Materials & Continua 2025, 84(1), 1769-1785. https://doi.org/10.32604/cmc.2025.062950

Received 31 December 2024; Accepted 21 March 2025; Issue published 09 June 2025

Abstract

Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts a graph-based structure to encapsulate deep local features. A graph convolutional network then amalgamates these features to understand the relationships among vehicle-related characteristics. Subsequently, we amalgamate feature maps from both the attention and graph-based branches for a more comprehensive representation of vehicle features. The framework then gauges feature similarities and ranks them, thus enhancing the accuracy of vehicle re-identification. Comprehensive qualitative and quantitative analyses on two publicly available datasets verify the efficacy of AG-GCN in addressing intra-class and inter-class variability issues.

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Keywords

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