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Person re-identification based on activation guided identity and attribute classification model

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Abstract

Attribute features can be exploited as high-level semantic features to help the model better express person characteristics and improve person re-identification (re-ID) performance. In this paper, a novel activation guided identity and attribute classification (AGIAC) model is proposed for person re-ID. AGIAC model includes the backbone, the mutex local activation (MLA) module and the branch-guided identity-attribute classification (BIAC) module. To fuse the global and local features, the backbone is designed as four branches. Based on the fact the different attributes are related to different regions, the BIAC module is designed, which employs the relevant branch to classify each attribute. To provide more specific information for each attribute in the BIAC model and increase the feature diversity of the four branches, the MLA module is constructed, which generates mutex activation area for each branch. The overall loss function of the AGIAC model is designed as the combination of identity classification loss, attribute classification loss and activation loss. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that the AGIAC model outperforms the state-of-the-art attribute-combined re-ID methods.

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Acknowledgments

The work was supported in part by the National Nature Science Foundation of China [grant number 61872030] and Major Science and Technology Innovation Project of Shandong Province [grant number 2019TSLH0206].

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Yanfeng Li, Bin Zhang, Jia Sun, Houjin Chen, Xiaodi Zhu & Jinlei Zhu

  2. Synthesis Electronic Technology Co., Ltd, Jinan, China

    Jinlei Zhu

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  1. Yanfeng Li
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  2. Bin Zhang
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  3. Jia Sun
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  4. Houjin Chen
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  5. Xiaodi Zhu
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  6. Jinlei Zhu
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Correspondence to Yanfeng Li.

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Li, Y., Zhang, B., Sun, J. et al. Person re-identification based on activation guided identity and attribute classification model. Multimed Tools Appl 80, 14961–14977 (2021). https://doi.org/10.1007/s11042-021-10545-4

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  • DOI: https://doi.org/10.1007/s11042-021-10545-4

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