@article{Sun2025, 
author = {Ya-Jie Sun and Li-Wei Qiao and Sai Ji},
title = {AG-GCN: Vehicle Re-Identification Based on Attention-Guided Graph Convolutional Network},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {84},
number = {1},
pages = {1769-1785},
keywords = {Vehicle re-identification, a lightweight attention module, global features, local features, graph convolution network},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.062950},
doi = {10.32604/cmc.2025.062950},
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.}
}