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Low-resolution assisted three-stream network for person re-identification

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Abstract

In the commonly used datasets of person re-identification, the image quality is not uniform. Most existing methods on person re-identification mainly focus on the challenges caused by occlusion, view and pose variations, ignoring the diversity of person image quality. In this paper, we provide an intuitive solution to address this problem. Specifically, we generate low-resolution images by reducing the resolution of original person images and propose a low-resolution assisted three-stream network (LRAN) to fuse the extracted person features from original RGB images, low-resolution images and greyscale images into a more robust feature as the final person representation. In this way, the model eliminates the impact of image quality differences to some extent. Experimental results demonstrate that the proposed method achieves the state-of-the-art results on Market-1501, DukeMTMC-reID and CUHK03-NP datasets.

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Acknowledgements

The work described in this paper was partially supported by the National Natural Science Foundation of China (Grant No. 61772093 ), Graduate Research and Innovation Foundation of Chongqing, China (Grant No. CYS18065), Chongqing Research Program of Basic Science & Frontier Technology (Grant No. cstc2018jcyjAX0 410), the Chongqing Major Theme Program (Grant No. cstc2017zdcyzdzxX0002) and the Fundamental Research Funds for the Central Universities (Grant No. 2019CDYGYB014, Grant No. 2019CDCGRJ217 and 2019CDXYRJ0011 ).

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Authors and Affiliations

  1. Ministry of Education, Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Chongqing, 400044, China

    Yongxin Ge, Sheng Huang, Feiyu Chen & Hongxing Wang

  2. School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, China

    Jiahong Xie, Yongxin Ge, Junyin Zhang, Sheng Huang, Feiyu Chen & Hongxing Wang

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  1. Jiahong Xie
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  2. Yongxin Ge
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  3. Junyin Zhang
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  4. Sheng Huang
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  5. Feiyu Chen
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  6. Hongxing Wang
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Correspondence to Yongxin Ge.

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Xie, J., Ge, Y., Zhang, J. et al. Low-resolution assisted three-stream network for person re-identification. Vis Comput 38, 2515–2525 (2022). https://doi.org/10.1007/s00371-021-02127-0

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