A Dual-Branch Network for Few-Shot Vehicle Re-Identification With Enhanced Global and Local Features

Abstract

Traditional vehicle Re-Identification (Re-ID) methods mainly rely on large-size training samples to achieve better results. However, obtaining abundant training samples is challenging for applications of these methods to few-shot vehicle Re-ID in real-world traffic scenes. To address this challenge, we propose a dual-branch network (DB-Net) for few-shot vehicle Re-ID with enhanced global and local features. Our proposed method reduces the dependence of vehicle Re-ID network on a large number of training samples by improving its feature expression ability and feature quality, rather than synthesizing vehicle images in traditional methods. The proposed DB-Net, composed of global and local branches, extracts global appearance features and local detail features of the vehicle. A global feature optimization module in the DB-Net mines and maintains more global semantic information using convolution and concat operations. And a feature screening module selects the most relevant local features based on Mutual Information and Shell Sorting to enhance local features. Additionally, a local attention module assigns adaptive weights to enhance salient local regions. Our approach is evaluated on a new dataset (Veri-FS) with small sample sizes and poor illumination conditions. It outperforms state-of-the-art methods on VehicleID, VeRi-776, and Veri-FS datasets, demonstrating its effectiveness in few-shot vehicle Re-ID.