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Multi-view Geometry Distillation for Cloth-Changing Person ReID

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13534))

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Abstract

Most person re-identification (ReID) methods aim at retrieving people with unchanged clothes. Meanwhile, fewer studies work on the cloth-inconsistency problem, which is more challenging but useful in the real intelligent surveillance scenario. We propose a novel method, named Multi-View Geometry Distillation (MVGD), taking advantage of 3D priors to explore cloth-unrelated multi-view human information. Specifically, a 3D Grouping Geometry Graph Convolution Network (3DG\(^{3}\)) is proposed to extract ReID-specific geometry representation from the 3D reconstructed body mesh, which encodes shape, pose, and other geometry patterns from the 3D perspective. Then, we design a 3D-Guided Appearance Learning scheme to extract more accurate part features. Furthermore, we also adopt a Multi-View Interactive Learning module (MVIL) to fuse the different types of features together and extract high-level multi-view geometry representation. Finally, these discriminative features are treated as the teacher to guide the backbone by the distillation mechanism for better representations. Extensive experiments on three popular cloth-changing ReID datasets demonstrate the effectiveness of our method. The proposed method brings 9\(\%\) and 7.5\(\%\) gains in average in terms of rank-1 and mAP metrics against the baseline, respectively.

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Notes

  1. 1.

    Our 13 joints include head, shoulders, elbows, wrists, hips, knees, and ankles.

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, University of Science and Technology of China, Hefei, China

    Hanlei Yu, Bin Liu, Yan Lu, Qi Chu & Nenghai Yu

  2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Science, Beijing, China

    Hanlei Yu, Bin Liu, Yan Lu, Qi Chu & Nenghai Yu

Authors
  1. Hanlei Yu
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  2. Bin Liu
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  3. Yan Lu
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  4. Qi Chu
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  5. Nenghai Yu
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Corresponding author

Correspondence to Bin Liu .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi’an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Yu, H., Liu, B., Lu, Y., Chu, Q., Yu, N. (2022). Multi-view Geometry Distillation for Cloth-Changing Person ReID. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-18907-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18906-7

  • Online ISBN: 978-3-031-18907-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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