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Light Field Reconstruction Using Dynamically Generated Filters Read chapter Read first chapter

2020 | OriginalPaper | Chapter

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

Authors : Han Fang, Jun Chen, Qi Tian

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
Multi-branch Body Region Alignment Network for Person Re-identification
Authors
Han Fang
Jun Chen
Qi Tian
Copyright Year
2020
Book
MultiMedia Modeling

Print ISBN: 978-3-030-37730-4

Electronic ISBN: 978-3-030-37731-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37731-1_28