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Learning discriminative features for person re-identification via multi-spectral channel attention

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Abstract

Person re-identification (Re-ID) aims to match a particular person captured by different cameras, which has great potential in video surveillance. However, Re-ID is still challenging due to occlusions, misalignment, background clutter, viewpoint changes, etc. To relieve these issues, this paper presents a multi-spectral channel attention network (MSCANet) to learn discriminative features for Re-ID. First, to better compress channels and explore the information left out by global average pooling (GAP), we employ multi-spectral channel attention (MSCA) to generalize the channel attention into the frequency domain. Second, to better capture more coarse and fine-grained clues, we design an improved attention pyramid (IAP) module which uses MSCA at the shallow level of the IAP to explore information lost by GAP so that more useful information can be introduced in attention learning. Sufficient experiments demonstrate the competitive performances of our MSCANet on the Market-1501 and DukeMTMC datasets. The mAP and Rank-1 accuracy of our model reach 89.3/95.8% and 80.2/89.9%, respectively

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Data availability

The original datasets have been published online. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (No. 62102320) and the Fundamental Research Funds for the Central Universities (No. D5000210737)

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Qianyue Duan, Zhenwu Hu, Minghao Lu & Huanjie Tao

  2. Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University), Ministry of Education, Xi’an, 710129, China

    Huanjie Tao

  3. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an, 710129, China

    Huanjie Tao

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  1. Qianyue Duan
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  2. Zhenwu Hu
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  3. Minghao Lu
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Correspondence to Huanjie Tao.

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Duan, Q., Hu, Z., Lu, M. et al. Learning discriminative features for person re-identification via multi-spectral channel attention. SIViP 17, 3019–3026 (2023). https://doi.org/10.1007/s11760-023-02522-1

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  • DOI: https://doi.org/10.1007/s11760-023-02522-1

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