  In recent years, vehicle re-identification (Re-ID) has gained increasing
importance in various applications such as assisted driving systems, traffic
flow management, and vehicle tracking, due to the growth of intelligent
transportation systems. However, the presence of extraneous background
information and occlusions can interfere with the learning of discriminative
features, leading to significant variations in the same vehicle image across
different scenarios. This paper proposes a method, named graph network based on
dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel
approach for constructing adjacency matrices to capture spatial relationships
of local features and reduce background noise. Specifically, the proposed
method divides the extracted vehicle features into different patches as nodes
within the graph network. A spatial attention-based similarity adjacency matrix
generation (SASAMG) module is employed to compute similarity matrices of nodes,
and a dynamic erasure operation is applied to disconnect nodes with low
similarity, resulting in similarity adjacency matrices. Finally, the nodes and
similarity adjacency matrices are fed into graph networks to extract more
discriminative features for vehicle Re-ID. Experimental results on public
datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed
method compared with recent works.
