  Person re-identification (person re-ID) aims at matching target person(s)
grabbed from different and non-overlapping camera views. It plays an important
role for public safety and has application in various tasks such as, human
retrieval, human tracking, and activity analysis. In this paper, we propose a
new network architecture called Hierarchical Cross Network (HCN) to perform
person re-ID. In addition to the backbone model of a conventional CNN, HCN is
equipped with two additional maps called hierarchical cross feature maps. The
maps of an HCN are formed by merging layers with different resolutions and
semantic levels. With the hierarchical cross feature maps, an HCN can
effectively uncover additional semantic features which could not be discovered
by a conventional CNN. Although the proposed HCN can discover features with
higher semantics, its representation power is still limited. To derive more
general representations, we augment the data during the training process by
combining multiple datasets. Experiment results show that the proposed method
outperformed several state-of-the-art methods.
