Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Under the low illumination condition, the performance of person Re-ID methods usually sharply deteriorates. To address the low illumination challenge in nighttime person Re-ID, this paper proposes an Illumination Distillation Framework (IDF), which utilizes illumination enhancement and illumination distillation schemes to promote the learning of Re-ID models. Specifically, IDF consists of a master branch, an illumination enhancement branch, and an illumination distillation module. The master branch is used to extract the features from a nighttime image. The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features. However, nighttime and enhanced features usually contain data noise due to unstable lighting conditions and enhancement failures. To fully exploit the complementary benefits of nighttime and enhanced features while suppressing data noise, we propose an illumination distillation module. In particular, the illumination distillation module fuses the features from two branches through a bottleneck fusion model and then uses the fused features to guide the learning of both branches in a distillation manner. In addition, we build a real-world nighttime person Re-ID dataset, named Night600, which contains 600 identities captured from different viewpoints and nighttime illumination conditions under complex outdoor environments. Experimental results demonstrate that our IDF can achieve state-of-the-art performance on two nighttime person Re-ID datasets (i.e., Night600 and Knight ). We will release our code and dataset at https://github.com/Alexadlu/IDF.

翻译：夜间行人重识别（夜间场景下的行人重识别）是视觉监控中非常重要且具有挑战性的任务，但尚未得到充分研究。在低光照条件下，行人重识别方法的性能通常会急剧下降。为解决夜间行人重识别中的低光照挑战，本文提出了一种光照蒸馏框架（IDF），该框架利用光照增强与光照蒸馏策略来促进重识别模型的学习。具体而言，IDF由主分支、光照增强分支和光照蒸馏模块组成。主分支用于从夜间图像中提取特征。光照增强分支首先通过非线性曲线映射方法从夜间图像中估计增强图像，再提取增强特征。然而，由于光照条件不稳定和增强失败，夜间特征与增强特征通常包含数据噪声。为充分利用夜间特征与增强特征的互补优势同时抑制数据噪声，我们提出光照蒸馏模块。该模块通过瓶颈融合模型融合两个分支的特征，并采用蒸馏方式利用融合特征指导两个分支的学习。此外，我们构建了真实场景的夜间行人重识别数据集Night600，该数据集包含在复杂室外环境下不同视角与夜间光照条件下采集的600个行人身份。实验结果表明，我们的IDF在两个夜间行人重识别数据集（即Night600和Knight）上均达到了最先进性能。代码与数据集将在https://github.com/Alexadlu/IDF 开源。