Reducing the gap between modalities is key to RGB-Infrared cross-modality person re-identification. In this paper, we propose an architecture based on the Deep Constrains Space of Medium Modality (DCSMM) for RGB-Infrared person re-identification. Specifically, a Medium Modality Network (MMN) is proposed to extract fused features of RGB and grayscale images, and we combine the fused features with infrared features for constraint. In addition, we also propose a loss function termed Domain Alignment and ID Consistency Loss (DAIC), which constrains the differences between the medium modality and the infrared modality as well as within single-modality in terms of instance level. Finally, in the high-level semantic stage, we also propose a Spatial Barycenter Margin Loss (SBM) based on each identity barycenter to constrain the feature space with different identities. The proposed method is validated on two large-scale datasets SYSU-MM01 and RegDB for cross-modality person re-identification, the results show that it achieves superior performance compared with the state-of-the-art methods.

减少模态之间的差距是 RGB-Infrared 跨模态人员重新识别的关键。在本文中，我们提出了一种基于中模态深度约束空间 (DCSMM) 的架构，用于 RGB 红外人员重新识别。具体来说，提出了一种中等模态网络（MMN）来提取RGB和灰度图像的融合特征，并将融合特征与红外特征结合进行约束。此外，我们还提出了一种称为域对齐和 ID 一致性损失 (DAIC) 的损失函数，它在实例级别方面限制了介质模态和红外模态之间以及单模态之间的差异。最后，在高级语义阶段，我们还提出了基于每个身份重心的空间重心边际损失（SBM），以约束具有不同身份的特征空间。所提出的方法在两个大规模数据集 SYSU-MM01 和 RegDB 上进行了验证，用于跨模态人员重新识别，结果表明，与最先进的方法相比，它取得了优越的性能。