Multi-granularity Pose Fusion Network with Views for Person Re-identification

Person re-identification (re-ID), aiming to recognize a person of interest across different cameras, is a well-known challenge because of the vast variation in human poses. Existing pose-driven re-ID methods utilize keypoints, which are considered fine-grained local information, inadequate to capture global pose characteristics. In this paper we propose multi-granularity pose fusion network (MGPFNet), to address the pose variation problem. In particular we propose the use of view information in re-ID. A pose in MGPFNet is multi-granular containing the coarse-grained view and fine-grained keypoint information. Two encoding methods, namely pose concatenation encoding and pose independent encoding, are proposed to incorporate both coarse-grained views information and fine-grained keypoints information while alleviating the impact of pose variations. Extensive experiments on three benchmark datasets with views demonstrates the effectiveness of MGPFNet which outperforms state-of-the-art methods in most cases, confirming the benefit of using views in re-ID.