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A siamese pedestrian alignment network for person re-identification

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Abstract

Deep learning methods show strong ability in extracting high-level features for images in the field of person re-identification. The produced features help inherently distinguish pedestrian identities in images. However, on deep learning models over-fitting and discriminative ability of the learnt features are still challenges for person re-identification. To alleviate model over-fitting and further enhance the discriminative ability of the learnt features, we propose siamese pedestrian alignment networks (SPAN) for person re-identification. SPAN employs two streams of PAN (pedestrian alignment networks) to increase the size of network inputs over limited training samples and effectively alleviate network over-fitting in learning. In addition, a verification loss is constructed between the two PANs to adjust the relative distance of two input pedestrians of the same or different identities in the learned feature space. Experimental verification is conducted on six large person re-identification data sets and the experimental results demonstrate the effectiveness of the proposed SPAN for person re-identification.

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their very competent comments and suggestions. An earlier version of this paper was presented at the Chinese Conference on Pattern Recognition and Computer Vision(PRCV 2019).

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No.2018XKQYMS27).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Yi Zheng, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu & Ying Chen

  2. Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Yi Zheng, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu & Ying Chen

  3. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Meng Jian

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  1. Yi Zheng
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Correspondence to Yong Zhou.

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Zheng, Y., Zhou, Y., Zhao, J. et al. A siamese pedestrian alignment network for person re-identification. Multimed Tools Appl 80, 33951–33970 (2021). https://doi.org/10.1007/s11042-021-11302-3

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