Multi-type Features Network for Person Re-identification

被引：0

作者：

Wang P. ^{[1
]}

Song X. ^{[1
]}

Wu X. ^{[1
]}

Yu D. ^{[2
]}

机构：

[1] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi

[2] School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 10期

基金：

中国博士后科学基金; 中国国家自然科学基金; 国家重点研发计划;

关键词：

Attention Mechanism; Feature Partition; Multi-type Features; Person Re-identification;

D O I：

10.16451/j.cnki.issn1003-6059.202010002

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

The attention mechanism is effective in person re-identification. However, the performance of the combined use of different types of attention mechanisms needs to be improved, such as spatial attention and self-attention. An improved convolutional block attention model(CBAM-PRO) is proposed, and then a multi-type features network(MTFN) is proposed. The features of different interested domains are extracted through the integration of CBAM-Pro and self-attention mechanism, and the local features with different granularities are introduced concurrently to perform person re-identification jointly. The validity and reliability of MTFN are verified by the experiments on the existing general benchmark datasets. © 2020, Science Press. All right reserved.

引用

页码：879 / 888

页数：9

共 34 条

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