NDLI: Mutual reinforced part-aligned network for person re-identification

Content Provider	IET Digital Library
Author	Liu, Yongwen An, Gaoyun Zheng, Zhenxing
Abstract	Part misalignment of the human body caused by complex variations in viewpoint and pose makes person re-identification be a fundamental challenging task. In this Letter, the authors propose a new Part-Aligned Network (PAN) stacked by Part-Aligned Module (PAM) to learn the effective representation for person re-identification. Specifically, PAN is constructed by stacking several PAMs which is simultaneously comprised of an appearance branch and a part branch. The appearance branch performs spatial modelling while the part branch captures human part information simultaneously. Appearance features and part features are then combined to explore the implicit complementary advantages and these two branches are learned in a mutually reinforcing way. Extensive experiments are conducted on the three most popular datasets: Market-1501, DukeMTMC-reID, and CUHK03. The experimental results demonstrate that the proposed PAN achieves superior performance to the existing state-of-the-art methods.
Starting Page	595
Ending Page	597
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 10, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.0363
Journal	Electronics Letters
Publisher Date	2019-04-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Appearance Branch Appearance Features Branches Computer Vision And Image Processing Technique Fundamental Challenging Task Human Body Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Mutually Reinforcing Way PAM PAN Part Branch Captures Human Part Information Part Feature Part Misalignment Part-Aligned Module Part-aligned Network Person Re-identification Pose Estimation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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