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Multi-level feature learning with attention for person re-identification

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Abstract

Person re-identification (re-ID) aims to match a specific person in a large gallery with different cameras and locations. Previous part-based methods mainly focus on part-level features with uniform partition, which increases learning ability for discriminative feature but not efficient or robust to scenarios with large variances. To address this problem, in this paper, we propose a novel feature fusion strategy based on traditional convolutional neural network. Then, a multi-branch deeper feature fusion network architecture is designed to perform discriminative learning for three semantically aligned region. Based on it, a novel self-attention mechanism is employed to softly assign corresponding weights to the semantic aligned feature during back-propagation. Comprehensive experiments have been conducted on several large-scale benchmark datasets, which demonstrates that proposed approach yields consistent and competitive re-ID accuracy compared with current single-domain re-ID methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Project(Grant No.61977045), the National Defense Pre-Research Foundation of China(Grant No.513110501). The authors would like to thank the anonymous reviewers for their valuable suggestions and constructive criticism.

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Suncheng Xiang, Yuzhuo Fu, Hao Chen, Wei Ran & Ting Liu

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  1. Suncheng Xiang
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  2. Yuzhuo Fu
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  3. Hao Chen
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  4. Wei Ran
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  5. Ting Liu
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Correspondence to Suncheng Xiang.

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Xiang, S., Fu, Y., Chen, H. et al. Multi-level feature learning with attention for person re-identification. Multimed Tools Appl 79, 32079–32093 (2020). https://doi.org/10.1007/s11042-020-09569-z

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  • DOI: https://doi.org/10.1007/s11042-020-09569-z

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