Occluded person re-identification (ReID) aims to match occluded person images
to holistic ones across dis-joint cameras. In this paper, we propose a novel
framework by learning high-order relation and topology information for
discriminative features and robust alignment. At first, we use a CNN backbone
and a key-points estimation model to extract semantic local features. Even so,
occluded images still suffer from occlusion and outliers. Then, we view the
local features of an image as nodes of a graph and propose an adaptive
direction graph convolutional (ADGC)layer to pass relation information between
nodes. The proposed ADGC layer can automatically suppress the message-passing
of meaningless features by dynamically learning di-rection and degree of
linkage. When aligning two groups of local features from two images, we view it
as a graph matching problem and propose a cross-graph embedded-alignment (CGEA)
layer to jointly learn and embed topology information to local features, and
straightly predict similarity score. The proposed CGEA layer not only take full
use of alignment learned by graph matching but also re-place sensitive
one-to-one matching with a robust soft one. Finally, extensive experiments on
occluded, partial, and holistic ReID tasks show the effectiveness of our
proposed method. Specifically, our framework significantly outperforms
state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.

本篇论文提出了一种新颖的框架，通过学习高阶关系和拓扑信息来实现人物重识别，其中采用了 CNN 和关键点估计模型分别从语义和局部信息两个角度进行特征提取，并将其视为图的节点，提出了自适应方向图卷积层和交叉图嵌入对齐层，通过优化图匹配和节点嵌入来最终匹配人员并在遮挡的数据集上取得了优越的效果。