Attribute Guided Graph Convolutional Network with Stronger Generalization for Person Re-identification

Abstract

Person re-identification(ReID) aims to match and track people in a surveillance system with non-overlapping camera views. It is a key challenge for person ReID to learn robust and discriminative person representations. However, in the real world, similar person appearance, different image angles and changing person attributes make the task very difficult. To tackle this problem, we propose the attribute guided graph convolutional networks(AG-GCN) to design a model with stronger generalization. Specifically, an attribute transfer module is introduced into the framework to revise person attributes to obtain diverse person feature expression. In addition, we apply graph convolutional networks to combine attributes with body parts as a more fine-grained representation of the person. The experiment results conducted on Market 1501 and DukeMTMCReID datasets show that our method outperforms state-of-the-art attribute-based methods on a single dataset and generalizes better on other datasets.