The primary color profile of the same identity is assumed to remain
consistent in typical Person Re-identification (Person ReID) tasks. However,
this assumption may be invalid in real-world situations and images hold variant
color profiles, because of cross-modality cameras or identity with different
clothing. To address this issue, we propose Color Space Learning (CSL) for
those Cross-Color Person ReID problems. Specifically, CSL guides the model to
be less color-sensitive with two modules: Image-level Color-Augmentation and
Pixel-level Color-Transformation. The first module increases the color
diversity of the inputs and guides the model to focus more on the non-color
information. The second module projects every pixel of input images onto a new
color space. In addition, we introduce a new Person ReID benchmark across RGB
and Infrared modalities, NTU-Corridor, which is the first with privacy
agreements from all participants. To evaluate the effectiveness and robustness
of our proposed CSL, we evaluate it on several Cross-Color Person ReID
benchmarks. Our method surpasses the state-of-the-art methods consistently. The
code and benchmark are available at: this https URL

通过颜色空间学习（CSL）解决了交叉颜色人物再识别问题，在不同模态的图像中增加颜色多样性，通过图像级颜色增强和像素级颜色转换两个模块实现，在 RGB 和红外模态的 NTU-Corridor 基准测试上验证了其有效性和鲁棒性。