Vehicle Re Identification

Vehicle re-identification (ReID) aims to match the same vehicle across different cameras, a crucial task for intelligent transportation systems and smart city initiatives. Current research focuses on improving accuracy and efficiency using deep learning models, including convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating attention mechanisms and feature fusion techniques to address challenges like viewpoint variations and inter-class similarity. These advancements enhance the reliability of vehicle tracking and identification across camera networks, impacting applications such as traffic monitoring, security, and autonomous driving. Furthermore, research explores unsupervised and continual learning approaches to reduce reliance on large annotated datasets and improve model generalization.