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Pedestrian Re-recognition Based on Memory Network and Graph Structure

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Digital Multimedia Communications (IFTC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1766))

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Abstract

Computer interaction and public safety have great research significance and practical value. Because of the problem that the recurrent neural network used in the existing literature will produce gradient disappearance and gradient explosion when the video sequence is long. Then, we propose a Person Re-identification Network with ConvLSTM (CLPRN) network based on convolutional long and short-term memory networks to solve the short-term memory problem. And then, aiming at the problem of information fusion between frames, we propose a Person Re-identification Network with Graph Convolution (GCPRN) network based on the graph structure, introduce a multi-header attention mechanism, and measure the relationship between frames. The experimental results shows that the Rank 1 of the GCPRN network on iLIDS Video re-identification (iLIDS-VID) dataset reaches \(70.58\%\) and Rank 5 reached \(81.20\%\), surpassing the Unsupervised Tracklet Association Learning (UTAL) and Temporal Knowledge Propagation (TKP) algorithm that reached a high level on the iLIDS-VID dataset.

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Acknowledgements

This study was supported by the Hunan Province Natural Science Foundation (grant number 2022JJ30673).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, China

    Yunfeng Zhang, Xiaopeng Li, Shihua Zhu, Muzhou Hou & Cong Cao

  2. Graduate School of Hebei University of Geosciences, Shijiazhuang, China

    Jiaqi Cao

  3. Department of Dermatology of Xiangya Hospital, Central South University, Changsha, China

    Jiarui Ou

Authors
  1. Yunfeng Zhang
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  2. Jiaqi Cao
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  3. Xiaopeng Li
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  4. Jiarui Ou
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  5. Shihua Zhu
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  6. Muzhou Hou
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  7. Cong Cao
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Corresponding author

Correspondence to Cong Cao .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Jia Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Zhang, Y. et al. (2023). Pedestrian Re-recognition Based on Memory Network and Graph Structure. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_1

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  • DOI: https://doi.org/10.1007/978-981-99-0856-1_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0855-4

  • Online ISBN: 978-981-99-0856-1

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