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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Acknowledgement
This research was partially supported by National Key R&D Program of China (2017YFC0803700), National Nature Science Foundation of China (U1611461, 61876135), Hubei Province Technological Innovation Major Project (2017AAA123, 2018AAA062), and Nature Science Foundation of Jiangsu Province (BK20160386).
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Fang, H., Chen, J., Tian, Q. (2020). Multi-branch Body Region Alignment Network for Person Re-identification. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_28
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DOI: https://doi.org/10.1007/978-3-030-37731-1_28
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