用于行人重识别的多类型特征网络

doi:10.16451/j.cnki.issn1003-6059.202010002

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Abstract 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.

Key words： Person Re-identification Attention Mechanism Feature Partition Multi-type Features

Received: 05 August 2020

ZTFLH:

TP391.41

Fund:; National Key Research and Development Program of China(No.2017YFC1601800), National Natural Science Foundation of China(No.61876072), China Postdoctoral Science Foundation(No.2018T110441), Six Talent Peaks Project in Jiangsu Province(No.XYDXX-012)

Corresponding Authors: SONG Xiaoning, Ph.D., professor. His research interests include pattern recognition,image processing, artificial intelligence and computer vision.

About author:: WANG Peng, master student. His research interests include deep learning and computer vision.WU Xiaojun, Ph.D., professor. His research interests include pattern recognition, computer vision, artificial intelligence, fuzzy system and neural network.YU Dongjun, Ph.D., professor. His research interests include machine learning, pattern recognition, neural networks and bioinformatics.

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	WANG Peng
	SONG Xiaoning
	WU Xiaojun
	YU Dongjun

Cite this article:

WANG Peng,SONG Xiaoning,WU Xiaojun等. Multi-type Features Network for Person Re-identification[J]. , 2020, 33(10): 879-888.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202010002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I10/879

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