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FV-REID: A Benchmark for Federated Vehicle Re-identification

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Biometric Recognition (CCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14463))

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Abstract

Vehicle re-identification is a crucial research direction in computer vision for constructing intelligent transportation systems and smart cities. However, privacy concerns pose significant challenges, such as personal information leakage and potential risks of data sharing. To address these challenges, we propose a federated vehicle re-identification (FV-REID) benchmark that protects vehicle privacy while exploring re-identification performance. The benchmark includes a multi-domain dataset and a federated evaluation protocol that allows clients to upload model parameters to the server without sharing data. We also design a baseline federated vehicle re-identification method called FVVR, which employs federated-averaging to facilitate model interaction. Our experiments on the FV-REID benchmark reveal that (1) the re-identification performance of the FVVR model is typically weaker than that of non-federated learning models and is prone to significant fluctuations and (2) the difference in re-identification performance between the FVVR model and the non-federated learning model would be more pronounced on a small-scale client dataset compared to a large-scale client dataset.

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Acknowledgements

This work was supported in part by the National Key R &D Program of China under Grant 2021YFE0205400, in part by the National Natural Science Foundation of China under Grant 61976098, in part by the Natural Science Foundation for Outstanding Young Scholars of Fujian Province under Grant 2022J06023, in part by the Key Program of Natural Science Foundation of Fujian Province under Grant 2023J02022, in part by the High-level Talent Innovation and Entrepreneurship Project of Quanzhou City under Grant 2023C013R.

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Authors and Affiliations

  1. College of Engineering, Huaqiao University, Quanzhou, 362021, People’s Republic of China

    Linhan Huang, Liangtai Zhou, Jianqing Zhu & Huanqiang Zeng

  2. College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, People’s Republic of China

    Qianqian Zhao

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  1. Linhan Huang
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  2. Qianqian Zhao
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  3. Liangtai Zhou
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  4. Jianqing Zhu
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  5. Huanqiang Zeng
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Corresponding author

Correspondence to Jianqing Zhu .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Wei Jia

  2. South China University of Technology, Guangzhou, China

    Wenxiong Kang

  3. China University of Mining and Technology, Xuzhou, China

    Zaiyu Pan

  4. Shandong University, Jinan, China

    Xianye Ben

  5. China University of Mining and Technology, Xuzhou, China

    Zhengfu Bian

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

    Shiqi Yu

  7. Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

  8. China University of Mining and Technology, Xuzhou, China

    Jun Wang

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

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Huang, L., Zhao, Q., Zhou, L., Zhu, J., Zeng, H. (2023). FV-REID: A Benchmark for Federated Vehicle Re-identification. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_37

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  • DOI: https://doi.org/10.1007/978-981-99-8565-4_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8564-7

  • Online ISBN: 978-981-99-8565-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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