In the commonly used datasets of person re-identification, the image quality is not uniform. Most existing methods on person re-identification mainly focus on the challenges caused by occlusion, view and pose variations, ignoring the diversity of person image quality. In this paper, we provide an intuitive solution to address this problem. Specifically, we generate low-resolution images by reducing the resolution of original person images and propose a low-resolution assisted three-stream network (LRAN) to fuse the extracted person features from original RGB images, low-resolution images and greyscale images into a more robust feature as the final person representation. In this way, the model eliminates the impact of image quality differences to some extent. Experimental results demonstrate that the proposed method achieves the state-of-the-art results on Market-1501, DukeMTMC-reID and CUHK03-NP datasets.

在常用的人物重新识别数据集中，图像质量不均匀。现有的大多数关于人物重新识别的方法主要集中在遮挡，视野和姿势变化所引起的挑战上，而忽略了人物图像质量的多样性。在本文中，我们提供了解决此问题的直观解决方案。具体来说，我们通过降低原始人物图像的分辨率来生成低分辨率图像，并提出了一种低分辨率辅助三流网络（LRAN），以将提取自原始RGB图像，低分辨率图像和灰度图像的人物特征融合为一个作为最终人员表示的更强大的功能。这样，该模型在某种程度上消除了图像质量差异的影响。