Transformer vehicle re-identification of intelligent transportation system under carbon neutral target

Abstract

Vehicle re-identification technology plays a pivotal role in intelligent transportation systems, contributing significantly to improved traffic scheduling and reduced net-zero emissions. It holds immense potential for advancing carbon neutrality efforts. By accurately recognizing and tracking vehicles, this technology optimizes traffic flow, enhances energy efficiency, reduces carbon emissions, and fosters sustainable transportation development. However, prevailing CNN-based feature extraction methods often lack the capability to associate global features, while Transformer-based approaches tend to overlook crucial local feature differences. Effectively mining local features and integrating them with global features are critical aspects of successful vehicle re-identification techniques. To tackle this challenge, we present a novel approach, the Locally Significant Feature Rearrangement (LSFR) module, based on the Swin Transformer model. This module enhances the learning of local features by initially focusing on specific regions using a single-shot multi-box detector (SSD) and employing an adaptive attention learning mechanism to highlight fine-grained local features. Subsequently, the salient local features are rearranged to prioritize their importance, and their embedding layers are fused with global features in a recombined manner. Our experimental results on the VeRi-776 and VehicleID benchmark datasets validate the effectiveness of our proposed method as a reliable technique to support intelligent transportation systems and drive progress towards traffic carbon neutralization.