Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3573-3579.DOI: 10.11772/j.issn.1001-9081.2021122124

• ChinaVR 2021 • Previous Articles    

Cross‑resolution person re‑identification by generative adversarial network based on multi‑granularity features

Yanbing GENG, Yongjian LIAN()   

  1. School of Data Science and Technology,North University of China,Taiyuan Shanxi 030051,China
  • Received:2021-12-17 Revised:2022-02-28 Accepted:2022-03-07 Online:2022-05-17 Published:2022-11-10
  • Contact: Yongjian LIAN
  • About author:GENG Yanbing, born in 1980, Ph. D., lecturer. Her research interests include image processing, pattern recognition, artificial intelligence.
    LIAN Yongjian, born in 1975, Ph. D., lecturer. His research interests include image processing, pattern recognition, artificial intelligence, virtual reality.
  • Supported by:
    Natural Science Foundation of Shanxi Province(201901D111154)


耿艳兵, 廉永健()   

  1. 中北大学 大数据学院,太原 030051
  • 通讯作者: 廉永健
  • 作者简介:耿艳兵(1980—),女,河南漯河人,讲师,博士,CCF会员,主要研究方向:图像处理、模式识别、人工智能
  • 基金资助:


Existing Super Resolution (SR) reconstruction methods based on Generative Adversarial Network (GAN) for cross?resolution person Re?IDentification (ReID) suffer from deficiencies in both texture structure content recovery and feature consistency maintenance of the reconstructed images. To solve these problems, a cross?resolution pedestrian re?identification method based on multi?granularity information generation network was proposed. Firstly, a self?attention mechanism was introduced into multiple layers of generator to focus on multi?granularity stable regions with structural correlation, focusing on recovering the texture and structure information of the Low Resolution (LR) person image. At the same time, an identifier was added at the end of the generator to minimize the loss in different granularity features between the generated image and the real image during the training process, improving the feature consistency between the generated image and the real image in terms of features. Secondly, the self?attention generator and identifier were jointed, then they were optimized alternately with the discriminator to improve the generated image on content and features. Finally, the improved GAN and person re?identification network were combined to train the model parameters of the optimized network alternately until the model converged. Comparison Experimental results on several cross?resolution person re?identification datasets show that the proposed algorithm improves rank?1 accuracy on Cumulative Match Characteristic(CMC) by 10 percentage points on average, and has better performance in enhancing both content consistency and feature expression consistency of SR images.

Key words: cross?resolution, person Re?IDentification (ReID), Generative Adversarial Network (GAN), self?attention mechanism, multi?granularity feature



关键词: 跨分辨率, 行人重识别, 生成对抗网络, 自注意力机制, 多粒度特征

CLC Number: