Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 146-152.DOI: 10.3778/j.issn.1002-8331.1912-0103

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Person Re-identification Based on Deformable Mask Alignment Convolution Model

LIU Chang, QIU Weigen, ZHANG Lichen   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2021-03-01 Published:2021-03-02



  1. 广东工业大学 计算机学院,广州 510006


Person re-identification is an important research direction in the field of computer vision, and has extremely important application prospects in very wide fields such as video surveillance. An important challenge in person re-identification research is the problem of person image alignment. In this paper, a new deformable mask aligned deep convolutional neural network model is proposed using the fully convolutional network model and global average pooling operation. It not only solves the problem of person image alignment, but also implements multi-information fusion of person images. The method in this paper is verified on the two large data sets of Market-1501 and DukeMTMC-reID, and the overall accuracy rate has been greatly improved.

Key words: person re-identification, alignment, full convolution model, information fusion



关键词: 行人再识别, 对齐, 全卷积模型, 信息融合