Hierarchical Cross Network for Person Re-identification
This addresses the problem of matching persons across non-overlapping camera views for public safety applications, representing an incremental improvement.
The paper tackled person re-identification by proposing a Hierarchical Cross Network (HCN) that uses hierarchical cross feature maps to uncover additional semantic features, and it outperformed several state-of-the-art methods in experiments.
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.