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Improving person re-identification via attribute-identity representation and visual attention mechanism

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Abstract

Person re-identification, which aims to compare a person of interest as seen in a “probe” camera view to a “gallery” of candidates captured from a camera that does not overlap with the probe one, has increased significant attention in computer vision due to its application in surveillance and security. Various methods utilize the global information as the feature descriptors, which neglect the details in an image and may have a low accuracy of person re-identification and not much attention has been paid to suppressing the background information, which has an influence on person re-identification to some extent. Being aware of these problems, this paper concentrate on the appearance of a person by improving feature descriptors that shed light on a combination framework by fusing the attribute-identity discrimination network with the person discrimination network based on the visual attention mechanism. Experimental results on publicly available image benchmark data sets have demonstrated that the proposed combination framework can achieve competitive performances as compared with state-of-the-art algorithms in terms of accuracy and effectiveness.

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Funding

This work was supported in part by the National Natural Science Foundation of China (Nos. 61872032), in part by the Fundamental Research Funds for the Central universities (2019JBM020), in part by the Key R&D Program of Zhejiang Province (Nos. 2019C01068).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Honglin Quan, Songhe Feng & Congyan Lang

  2. School of Information Science and Engineering, Central South University, Changsha, China

    Baifan Chen

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  1. Honglin Quan
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  2. Songhe Feng
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  3. Congyan Lang
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  4. Baifan Chen
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Correspondence to Songhe Feng.

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Quan, H., Feng, S., Lang, C. et al. Improving person re-identification via attribute-identity representation and visual attention mechanism. Multimed Tools Appl 79, 7259–7278 (2020). https://doi.org/10.1007/s11042-019-08184-x

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