Journal
MULTIMEDIA MODELING (MMM 2020), PT I
Volume 11961, Issue -, Pages 341-352Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-37731-1_28
Keywords
Person re-identification; Keypoints detection; Feature fusion
Categories
Funding
- National Key RAMP
- D Program of China [2017YFC0803700]
- National Nature Science Foundation of China [U1611461, 61876135]
- Hubei Province Technological Innovation Major Project [2017AAA123, 2018AAA062]
- Nature Science Foundation of Jiangsu Province [BK20160386]
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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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