Person re-identification (re-id) is the task of recognizing images of the same pedestrian captured by different cameras with non-overlapping views. Person re-id is a challenging task due to the existence of large view variations, such as spatial misalignment, background clutter and human poses change. In this paper, we handle these challenges from the following two aspects: utilizing attention mechanism to alleviate misalignment problem and exploiting the complementary effects of global-local features for more stable pedestrian descriptors. Specifically, we first present a part-based attention model consisting of a channel attention block and a spatial attention block to sequentially refine the convolutional descriptors of person body parts. The channel and spatial attention blocks weight the channels and positions of body-part feature maps to spot the informative channels and regions, respectively. Then global full-body and local body-part of the refined feature maps are pooled into global and local representations, which are jointly trained using identity classification loss. We conduct extensive experiments on four standard benchmark datasets including Market1501, CUHK03, DukeMTMC-reID, and CUHK01, and the experimental results demonstrate the effectiveness of the presented method.

人员重新识别（re-id）是识别具有不重叠视图的不同摄像机捕获的同一行人图像的任务。由于存在较大的视图变化（例如空间未对准，背景混乱和人的姿势变化），因此人员重新识别是一项具有挑战性的任务。在本文中，我们从以下两个方面来应对这些挑战：利用注意力机制缓解不对准问题，并利用全局局部特征的互补效应获得更稳定的行人描述符。具体而言，我们首先提出一个基于部分的注意力模型，该模型由通道注意力块和空间注意力块组成，以依次完善人体部位的卷积描述符。通道和空间注意块对身体部位特征图的通道和位置进行加权，以分别发现信息丰富的通道和区域。然后，将经过精炼的特征图的全局全身和局部身体部分合并为全局和局部表示，使用身份分类损失对它们进行联合训练。我们对包括Market1501，CUHK03，DukeMTMC-reID和CUHK01在内的四个标准基准数据集进行了广泛的实验，实验结果证明了该方法的有效性。