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 finegrained features and the relationship

more »

... between features as much as possible from vehicle images. To address those problems, we propose a partial-aware and crosscorrelated 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 transformerbased 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.