Spatial Preserved Graph Convolution Networks for Person Re-identification

Abstract

Person Re-identification is a very challenging task due to inter-class ambiguity caused by similar appearances, and large intra-class diversity caused by viewpoints, illuminations, and poses. To address these challenges, in this article, a graph convolution network based model for person re-identification is proposed to learn more discriminative feature embeddings, where a graph-structured relationship between person images and person parts are together integrated. Graph convolution networks extract common characteristics of the same person, while pyramid feature embedding exploits parts relations and learns stable representation with each person image. We achieve a very competitive performance respectively on three widely used datasets, indicating that the proposed approach significantly outperforms the baseline methods and achieves the state-of-the-art performance.