基于多分支协作的行人重识别网络

doi:10.16451/j.cnki.issn1003-6059.202109008

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Abstract Designing multi-branch networks to learn rich feature representation is one of the important directions in person re-identification (Re-ID). Aiming at the limited feature representation learned by a single branch, a multi-branch cooperative network for person Re-ID (BC-Net) is proposed. Powerful feature representation for person Re-ID is obtained by extracting features from four cooperative branches, local branch, global branch, relational branch and contrastive branch. The proposed network can be applied to different backbone networks. OSNet and ResNet are considered as the backbone of the proposed network for verification. Extensive experiments show that BC-Net achieves state-of-the-art performance on the popular Re-ID datasets.

Key words： Person Re-identification Feature Representation Deep Learning Multi-branch Network Architecture

Received: 22 February 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61701252)

Corresponding Authors: WU Xiaofu, Ph.D., professor. His research interests include information theory and coding, machine learning, computer vision, information security and cryptology.

About author:: ZHANG Lei, master student. His research interests include person re-identification.
ZHANG Suofei, Ph.D., lecturer. His research interests include image and video signal processing, machine learning and Internet of things.
YIN Zirui, master student. His research interests include machine learning.

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	ZHANG Lei
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Cite this article:

ZHANG Lei,WU Xiaofu,ZHANG Suofei等. Multi-branch Cooperative Network for Person Re-identification[J]. , 2021, 34(9): 853-862.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202109008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I9/853

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