The goal of occluded person re-identification (ReID) is to retrieve specific pedestrians in occluded situations. However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance. In our research, we introduce a new framework called PAB-ReID, which is a novel ReID model incorporating part-attention mechanisms to tackle the aforementioned issues effectively. Firstly, we introduce the human parsing label to guide the generation of more accurate human part attention maps. In addition, we propose a fine-grained feature focuser for generating fine-grained human local feature representations while suppressing background interference. Moreover, We also design a part triplet loss to supervise the learning of human local features, which optimizes intra/inter-class distance. We conducted extensive experiments on specialized occlusion and regular ReID datasets, showcasing that our approach outperforms the existing state-of-the-art methods.

翻译：遮挡人员重识别（ReID）的目标是在遮挡场景中检索特定行人。然而，遮挡人员ReID仍面临背景杂乱和局部特征表示质量低下的问题，这限制了模型的性能。在本研究中，我们引入了一种名为PAB-ReID的新框架，这是一种结合了部位注意力机制的新型ReID模型，能够有效解决上述问题。首先，我们利用人体解析标签来指导生成更准确的人体部位注意力图。此外，我们提出了一个细粒度特征聚焦器，用于生成精细的人体局部特征表示，同时抑制背景干扰。另外，我们还设计了一种部位三元组损失函数，用于监督人体局部特征的学习，从而优化类内/类间距离。我们在专门的遮挡和常规ReID数据集上进行了大量实验，结果表明我们的方法优于现有的最先进方法。