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.

翻译：近年来，随着智能交通系统的发展，车辆重识别（Re-ID）在辅助驾驶系统、交通流管理和车辆跟踪等应用场景中日益重要。然而，冗余背景信息和遮挡的存在会干扰判别性特征的学习，导致同一车辆图像在不同场景下出现显著差异。本文提出一种基于动态相似度邻接矩阵的图网络方法（DSAM-GN），该方法创新性地构建邻接矩阵以捕获局部特征的空间关系并减少背景噪声。具体而言，所提方法将提取的车辆特征划分为不同片段作为图网络中的节点；采用基于空间注意力的相似度邻接矩阵生成模块（SASAMG）计算节点间的相似度矩阵，并通过动态擦除操作断开低相似度节点间的连接，从而生成相似度邻接矩阵；最终将节点与相似度邻接矩阵输入图网络，提取更具判别性的特征用于车辆重识别。在公开数据集VeRi-776和VehicleID上的实验结果表明，与近期工作相比，该方法具有有效性。