Parallel Dense Vision Transformer and Augmentation Network for Occluded Person Re-identification

Occluded person re-identification (ReID) is a challenging computer vision task in which the goal is to identify specific pedestrians in occluded scenes across different devices. Some existing methods mainly focus on developing effective data augmentation and representation learning techniques to improve the performance of person ReID systems. However, existing data augmentation strategies can not make full use of the information in the training data to accurately simulate the occlusion scenario, resulting in poor generalization ability. Additionally, recent Vision Transformer (ViT)-based methods have been shown beneficial for addressing occluded person ReID as they have powerful representation learning ability, but they always ignore the information fusion between different levels of features. To alleviate these two issues, an improved ViT-based framework called Parallel Dense Vision Transformer and Augmentation Network (PDANet) is proposed to extract well and robustly features. We first design a parallel data augmentation strategy based on random stripe erasure to enrich the diversity of input sample for better cover real scenes through various processing methods, and improve the generalization ability of the model by learning the relationship between these different samples. We then develop a Densely Connected Vision Transformer (DCViT) module for feature encoding, which strengthens the feature propagation and improves the effectiveness of learning by establishing connections between different layers. Experimental results demonstrate the proposed method outperforms the existing methods on both the occluded person and the holistic person ReID benchmarks.