Abstract
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.
Abstract (translated)
遮挡人物识别（ReID）的目标是检索遮挡情况下的特定行人。然而，遮挡人物ReID仍然受到背景杂乱和低质量局部特征表示的限制，这限制了模型的性能。在我们的研究中，我们引入了一个新的框架PAB-ReID，这是一种新型的ReID模型，采用了部分注意机制来有效解决上述问题。首先，我们引入了人类解析标签来指导生成更准确的人的部分注意力图。此外，我们提出了一种细粒度特征关注器，用于在抑制背景干扰的同时生成细粒度的人局部特征表示。此外，我们还设计了一个部分三元组损失来指导人局部特征的学习，该损失优化了类内/类间距离。我们在专门的遮挡和普通ReID数据集上进行了广泛的实验，展示了我们的方法超越了现有最先进的方法。
URL
https://arxiv.org/abs/2404.03443
