Multi-branch Body Region Alignment Network for Person Re-identification

Person re-identification (Re-ID) aims to identify the same person images from a gallery set across different cameras. Human pose variations, background clutter and misalignment of detected human images pose challenges for Re-ID tasks. To deal with these issues, we propose a Multi-branch Body Region Alignment Network (MBRAN), to learn discriminative representations for person Re-ID. It consists of two modules, i.e., body region extraction and feature learning. Body region extraction module utilizes a single-person pose estimation method to estimate human keypoints and obtain three body regions. In the feature learning module, four global or local branch-networks share base layers and are designed to learn feature representation on three overlapping body regions and the global image. Extensive experiments have indicated that our method outperforms several state-of-the-art methods on two mainstream person Re-ID datasets.