  In this paper, we propose a novel method called AlignedReID that extracts a
global feature which is jointly learned with local features. Global feature
learning benefits greatly from local feature learning, which performs an
alignment/matching by calculating the shortest path between two sets of local
features, without requiring extra supervision. After the joint learning, we
only keep the global feature to compute the similarities between images. Our
method achieves rank-1 accuracy of 94.4% on Market1501 and 97.8% on CUHK03,
outperforming state-of-the-art methods by a large margin. We also evaluate
human-level performance and demonstrate that our method is the first to surpass
human-level performance on Market1501 and CUHK03, two widely used Person ReID
datasets.
