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Person re-identification based on multi-appearance model

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Abstract

Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61702471, No. 61872326, No.61672475); Shandong Provincial Natural Science Foundation (ZR2019MF044).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, China

    Lei Huang, Wenfeng Zhang, Jie Nie & Zhiqiang Wei

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  1. Lei Huang
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  2. Wenfeng Zhang
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  3. Jie Nie
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  4. Zhiqiang Wei
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Correspondence to Jie Nie.

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Huang, L., Zhang, W., Nie, J. et al. Person re-identification based on multi-appearance model. Multimed Tools Appl 80, 16413–16423 (2021). https://doi.org/10.1007/s11042-020-08927-1

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