Unsupervised Person Reidentification Using Stripe‐Driven Fusion Transformer Network
Zeyu Zang, Yang Liu, Shuang Liu, Zhong Zhang, Xinshan Zhu · IET Software · 2025
In recent years, some methods utilize a transformer as the backbone to model the long‐range context dependencies, reflecting a prevailing trend in unsupervised person reidentification (Re‐ID) tasks. However, they only explore the global information through interactive learning in the framework of the transformer, which ignores the learning of the part information in the interaction process for pedestrian images. In this study, we present a novel transformer network for unsupervised person Re‐ID, a stripe‐driven fusion transformer (SDFT), designed to simultaneously capture the global interaction and the part interaction when modeling the long‐range context dependencies. Meanwhile, we present a stripe‐driven regularization (SDR) to constrain the part aggregation features and the global features by considering the consistency principle from the aspects of the features and the clusters, aiming to improve the representational capacity of the features. Furthermore, to investigate the relationships between local regions of pedestrian images, we present a stripe‐driven contrastive loss (SDCL) to learn discriminative part features from the perspectives of pedestrian identity and stripes. The proposed method has undergone extensive validations on publicly available unsupervised person Re‐ID benchmarks, and the experimental results confirm its superiority and effectiveness.