Occlusion is unavoidable in real-world applications of person re-identification (ReID). To alleviate the occlusion problem, this work proposes the detection of the occluded and visible regions of the human body by suppressing the occluded region during feature generation and matching, and enhancing the significance of the visible region. This paper introduces a novel method based on pose-guided spatial attention (PGSA) and activation-based attention (AA) called dual-attention re-identification (DAReID). DAReID consists of a mask branch and a global branch and uses ResNet-50 as the backbone network. The mask branch uses PGSA to obtain the visible and occluded regions of a person and constructs pose guided coarse labels for the occluded region through keypoints of the human body, driving the network to obtain robust local features. The global branch obtains the visual activation levels of different regions through AA, and combines this with human pose information to define weighted local distances(WLD). The WLD learning strategy is applied to drive the network to learn new and more discriminative local features. Experimental results show that DAReID achieves comparable performance on the Market1501, DukeMTMC-reID, and CUHK-03 datasets. And on the Occluded-DukeMTMC dataset, DAReID outperforms the existing methods.

在现实世界中，重新使用个人识别码（ReID）不可避免地会出现遮挡。为了缓解遮挡问题，这项工作提出了通过在特征生成和匹配期间抑制遮挡区域并增强可见区域的重要性来检测人体的遮挡区域和可见区域的方法。本文介绍了一种基于姿势引导的空间注意（PGSA）和基于激活的注意（AA）的新方法，称为双重注意重新识别（DAReID）。DAReID由掩码分支和全局分支组成，并使用ResNet-50作为骨干网。遮罩分支使用PGSA获取人的可见区域和遮挡区域，并通过人体的关键点为遮挡区域构建姿势引导的粗略标签，从而驱动网络获得鲁棒的局部特征。全球分支通过AA获得不同区域的视觉激活水平，并将其与人体姿势信息相结合以定义加权局部距离（WLD）。WLD学习策略用于驱动网络学习新的和更具区分性的本地功能。实验结果表明，DAReID在Market1501，DukeMTMC-reID和CUHK-03数据集上具有可比的性能。在Occluded-DukeMTMC数据集上，DAReID优于现有方法。DukeMTMC-reID和CUHK-03数据集。在Occluded-DukeMTMC数据集上，DAReID优于现有方法。DukeMTMC-reID和CUHK-03数据集。在Occluded-DukeMTMC数据集上，DAReID优于现有方法。