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Dual semantic-aligned clustering for cross-domain person re-identification

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Abstract

Person re-identification (ReID) intends to link people with common identities captured by various cameras spread across a big, fragmented area. Due to the imbalance in the distribution of data between input and output variables and the absence of labeled information in the target domain, cross-domain person re-identification (ReID) is a high demanding assignment. This paper presents a contemporary dual semantic-aligned (DSA) clustering approach, which jointly learns latent semantic subspaces of source and target domains to group target samples. More specifically, we present an innovative subspace learning model which jointly finds intra-domain basis and inter-domain multi-granularity basis. After the subspaces are learned, target samples are correspondingly projected and clustered automatically in each subspace. The clusters allow the model to attribute imaginary labels to the target samples and then impose supervised constraints throughout the training phase. Experiment results on DukeMTMC-reID, Market-1501, and MSMT17 benchmarks validate that our scheme significantly outperformed the state-of-the-art schemes. Our approach obtains performance of 86.82%/63.06% (Duke\(\rightarrow\)Market), 76.3%/58.35% (Market\(\rightarrow\)Duke) and 52.97%/22.44% (Duke\(\rightarrow\)MSMT17) in Rank-1/mAP, correspondingly.

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Acknowledgements

This work was supported in part by the Macao Science and Technology Development Fund Project under contract No.0005/2021/AIR.

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Authors and Affiliations

  1. Macau University of Science and Technology, Avenida Wai Long, Macau, 999078, China

    Changshui Yang, Yan Liu, Qiang Liu & Wenyong Wang

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Xisaishan Road, Huzhou, 313001, Zhejiang, China

    Riaz Ullah Khan

  3. Fnetlink Tecnology Company Limited, Della Sinagna Street, Macau, 999078, China

    Bin Chen

Authors
  1. Changshui Yang
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  2. Yan Liu
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  3. Qiang Liu
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  4. Riaz Ullah Khan
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  5. Bin Chen
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  6. Wenyong Wang
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All the authors discussed the idea of the this work, as well as revised and reviewed the manuscript.

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Correspondence to Wenyong Wang.

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Yang, C., Liu, Y., Liu, Q. et al. Dual semantic-aligned clustering for cross-domain person re-identification. Multimedia Systems 29, 2351–2362 (2023). https://doi.org/10.1007/s00530-023-01111-z

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