Multi-Branch Context-Aware Network for Person Re-Identification

Abstract

Most existing methods on person re-identification pay redundant attention to global features or local features which ignore contextual dependencies which are equally important in representing pedestrian images. In this paper, we propose a Multi-Branch Context-Aware Network (MBCAN) for person re-identification to exploit rich context information. MBCAN learns global features and local-part features in two separate branches to take full advantages of both coarse-grained and fine-grained features. Additionally, two types of attention modules are introduced to capture contextual dependencies in spatial dimension and channel dimension, respectively. A module called feature vector extraction block is designed to find an efficient way to integrate features from coarse to fine. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on Market-1501, DukeMTMC-reID and CUHK03 datasets.