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Asian Conference on Computer Vision

ACCV 2022: Computer Vision – ACCV 2022 Workshops pp 213–225Cite as

Temporal Extension Topology Learning for Video-Based Person Re-identification

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13848))

Abstract

Video-based person re-identification aims to match the same identification from video clips captured by multiple non-overlapping cameras. By effectively exploiting both temporal and spatial clues of a video clip, a more comprehensive representation of the identity in the video clip can be obtained. In this manuscript, we propose a novel graph-based framework, referred as Temporal Extension Adaptive Graph Convolution (TE-AGC) which could effectively mine features in spatial and temporal dimensions in one graph convolution operation. Specifically, TE-AGC adopts a CNN backbone and a key-point detector to extract global and local features as graph nodes. Moreover, a delicate adaptive graph convolution module is designed, which encourages meaningful information transfer by dynamically learning the reliability of local features from multiple frames. Comprehensive experiments on two video person re-identification benchmark datasets have demonstrated the effectiveness and state-of-the-art performance of the proposed method.

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Author information

Authors and Affiliations

  1. Fujitsu Research and Development Center Co., Ltd, Beijing, China

    Jiaqi Ning, Fei Li & Rujie Liu

  2. Fujitsu Research, Fujitsu Limited, Kawasaki, Japan

    Shun Takeuchi & Genta Suzuki

Authors
  1. Jiaqi Ning
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  2. Fei Li
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  3. Rujie Liu
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  4. Shun Takeuchi
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  5. Genta Suzuki
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Corresponding author

Correspondence to Jiaqi Ning .

Editor information

Editors and Affiliations

  1. University of Tokyo, Tokyo, Japan

    Yinqiang Zheng

  2. Hacettepe University, Ankara, Türkiye

    Hacer Yalim Keleş

  3. Data61/CSIRO, Canberra, ACT, Australia

    Piotr Koniusz

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Ning, J., Li, F., Liu, R., Takeuchi, S., Suzuki, G. (2023). Temporal Extension Topology Learning for Video-Based Person Re-identification. In: Zheng, Y., Keleş, H.Y., Koniusz, P. (eds) Computer Vision – ACCV 2022 Workshops. ACCV 2022. Lecture Notes in Computer Science, vol 13848. Springer, Cham. https://doi.org/10.1007/978-3-031-27066-6_15

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  • DOI: https://doi.org/10.1007/978-3-031-27066-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27065-9

  • Online ISBN: 978-3-031-27066-6

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