Vehicle Re-Identification Based on Convolution and Vision Transformer Feature Fusion

Currently, surveillance cameras are extensively employed in public security, and vehicle re-identification has emerged as a burgeoning research area in computer vision. Nevertheless, vehicle re-identification grapples with the challenges of low intra-class similarity and high inter-class similarity. This study tackles these challenges by introducing a novel vehicle re-identification method that integrates convolution and vision transformer features. Specifically, channel-by-channel convolution is incorporated into the feedforward layer to bolster the extraction of local features. Concurrently, the information from the last layer's class token and other patches is fused to yield a comprehensive and rich featured representation. Experiments conducted on the VeRi776 and VehicleID datasets validate that the proposed method outperforms current state-of-the-art vehicle re-identification methods.