Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2182-2189.DOI: 10.11772/j.issn.1001-9081.2022060827

• Artificial intelligence • Previous Articles     Next Articles

Person re-identification method based on multi-modal graph convolutional neural network

Jiaming HE1, Jucheng YANG1(), Chao WU1, Xiaoning YAN2, Nenghua XU2   

  1. 1.College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China
    2.Shenzhen Softsz Technology Company Limited,Shenzhen Guangdong 518131,China
  • Received:2022-06-10 Revised:2022-09-02 Accepted:2022-09-09 Online:2022-10-11 Published:2023-07-10
  • Contact: Jucheng YANG
  • About author:HE Jiaming, born in 1995, M. S. candidate. His research interests include person re-identification.
    YANG Jucheng, born in 1980, Ph. D., professor. His research interests include image processing, pattern recognition.
    WU Chao, born in 1974, Ph. D., lecturer. His research interests include image processing, pattern recognition.
    YAN Xiaoning, born in 1989, M. S. His research interests include artificial intelligence of video image.
    XU Nenghua, born in 1982. His research interests include artificial intelligence of video image.


何嘉明1, 杨巨成1(), 吴超1, 闫潇宁2, 许能华2   

  1. 1.天津科技大学 人工智能学院,天津 300457
    2.深圳市安软科技股份有限公司,广东 深圳 518131
  • 通讯作者: 杨巨成
  • 作者简介:何嘉明(1995—),男,广东清远人,硕士研究生,CCF会员,主要研究方向:行人重识别;


Aiming at the problems that person textual attribute information is not fully utilized and the semantic relationships among the textual attributes are not mined in person re-identification, a person re-identification method based on multi-modal Graph Convolutional neural Network (GCN) was proposed. Firstly, Deep Convolutional Neural Network (DCNN) was used to learn person textual attributes and person image features. Then, with the help of the effective relationship mining ability of GCN, the textual attribute features and image features were treated as the input of GCN, and the semantic information of the textual attribute nodes was transferred through the graph convolution operation, so as to learn the implicit semantic relationship information among the textual attributes and incorporate this semantic information into image features. Finally, the robust person features were output by GCN. The multi-modal person re-identification method achieves the mean Average Precision (mAP) of 87.6% and the Rank-1 accuracy of 95.1% on Market-1501 dataset, and achieves the mAP of 77.3% and the Rank-1 accuracy of 88.4% on DukeMTMC-reID dataset, which verify the effectiveness of the proposed method.

Key words: person re-identification, multi-modal, Graph Convolutional neural Network (GCN), person textual attribute, potential semantic relationship



关键词: 行人重识别, 多模态, 图卷积神经网络, 行人文本属性, 隐含语义联系

CLC Number: