MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re...

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Autores principales:	Jing Mei, Huahu Xu, Yang Li, Minjie Bian, Yuzhe Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	person re-identification RGB–IR MFCNet SGA module Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/b28eea40d8a344c585c7657b81e6d421
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id	oai:doaj.org-article:b28eea40d8a344c585c7657b81e6d421
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spelling	oai:doaj.org-article:b28eea40d8a344c585c7657b81e6d4212021-11-25T17:39:58ZMFCNet: Mining Features Context Network for RGB–IR Person Re-Identification10.3390/fi131102901999-5903https://doaj.org/article/b28eea40d8a344c585c7657b81e6d4212021-11-01T00:00:00Zhttps://www.mdpi.com/1999-5903/13/11/290https://doaj.org/toc/1999-5903RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.Jing MeiHuahu XuYang LiMinjie BianYuzhe HuangMDPI AGarticleperson re-identificationRGB–IRMFCNetSGA moduleInformation technologyT58.5-58.64ENFuture Internet, Vol 13, Iss 290, p 290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	person re-identification RGB–IR MFCNet SGA module Information technology T58.5-58.64
spellingShingle	person re-identification RGB–IR MFCNet SGA module Information technology T58.5-58.64 Jing Mei Huahu Xu Yang Li Minjie Bian Yuzhe Huang MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
description	RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.
format	article
author	Jing Mei Huahu Xu Yang Li Minjie Bian Yuzhe Huang
author_facet	Jing Mei Huahu Xu Yang Li Minjie Bian Yuzhe Huang
author_sort	Jing Mei
title	MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
title_short	MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
title_full	MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
title_fullStr	MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
title_full_unstemmed	MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification
title_sort	mfcnet: mining features context network for rgb–ir person re-identification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b28eea40d8a344c585c7657b81e6d421
work_keys_str_mv	AT jingmei mfcnetminingfeaturescontextnetworkforrgbirpersonreidentification AT huahuxu mfcnetminingfeaturescontextnetworkforrgbirpersonreidentification AT yangli mfcnetminingfeaturescontextnetworkforrgbirpersonreidentification AT minjiebian mfcnetminingfeaturescontextnetworkforrgbirpersonreidentification AT yuzhehuang mfcnetminingfeaturescontextnetworkforrgbirpersonreidentification
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MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

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