DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification
This addresses vehicle re-identification for applications like traffic management, but it is incremental as it builds on existing graph network approaches with a novel adjacency matrix construction.
The paper tackled vehicle re-identification by proposing DSAM-GN, a graph network method that uses dynamic similarity adjacency matrices to reduce background noise and capture spatial relationships, achieving improved performance on VeRi-776 and VehicleID datasets compared to recent works.
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.