Parallelism Network with Partial-aware and Cross-correlated Transformer for Vehicle Re-identification

Abstract

Vehicle re-identification (ReID) aims to identify a specific vehicle in the dataset captured by non-overlapping cameras, which plays a great significant role in the development of intelligent transportation systems. Even though CNN-based model achieves impressive performance for the ReID task, its Gaussian distribution of effective receptive fields has limitations in capturing the long-term dependence between features. Moreover, it is crucial to capture fine-grained features and the relationship between features as much as possible from vehicle images. To address those problems, we propose a partial-aware and cross-correlated transformer model (PCTM), which adopts the parallelism network extracting discriminant features to optimize the feature representation for vehicle ReID. PCTM includes a cross-correlation transformer branch that fuses the features extracted based on the transformer module and feature guidance module, which guides the network to capture the long-term dependence of key features. In this way, the feature guidance module promotes the transformer-based features to focus on the vehicle itself and avoid the interference of excessive background for feature extraction. Moreover, PCTM introduced a partial-aware structure in the second branch to explore fine-grained information from vehicle images for capturing local differences from different vehicles. Furthermore, we conducted experiments on 2 vehicle datasets to verify the performance of PCTM.