Part-Based Attribute-Aware Network for Person Re-Identification

Despite the rapid progress over the past decade, person re-identification (reID) remains a challenging task due to the fact that discriminative features underlying different granularities are easily affected by illumination and camera-view variation. Most deep learning-based algorithms for reID extr...

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Autores principales:	Yan Zhang, Xusheng Gu, Jun Tang, Ke Cheng, Shoubiao Tan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Deep learning reID layered partition strategy person attribute recognition semantic feature Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fdb14800318c4dadb21c26d14b9fbd85
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id	oai:doaj.org-article:fdb14800318c4dadb21c26d14b9fbd85
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spelling	oai:doaj.org-article:fdb14800318c4dadb21c26d14b9fbd852021-11-19T00:02:13ZPart-Based Attribute-Aware Network for Person Re-Identification2169-353610.1109/ACCESS.2019.2912844https://doaj.org/article/fdb14800318c4dadb21c26d14b9fbd852019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8695817/https://doaj.org/toc/2169-3536Despite the rapid progress over the past decade, person re-identification (reID) remains a challenging task due to the fact that discriminative features underlying different granularities are easily affected by illumination and camera-view variation. Most deep learning-based algorithms for reID extract global embedding as the representation of the pedestrian from the convolutional neural network. Considering that person attributes are robust and informative to identify pedestrians. This paper proposes a multi-branch model, namely part-based attribute-aware network (PAAN), to leverage both person reID and attribute performance, which not only utilizes ID label visible to the whole image but also utilizes attribute information. In order to learn discriminative and robust global representation which is invariant to the fact mentioned above, we resort to global and local person attributes to build global and local representation, respectively, utilizing our proposed layered partition strategy. Our goal is to exploit global or local semantic information to guide the optimization of global representation. Besides, in order to enhance the global representation, we design a semantic bridge replenishing mid-level semantic information for the final representation, which contains high-level semantic information. The extensive experiments are conducted to demonstrate the effectiveness of our proposed approach on two large-scale person re-identification datasets including Market-1501 and DukeMTMC-reID, and our approach achieves rank-1 of 92.40% on Market-1501 and 82.59% on DukeMTMC-reID showing strong competitiveness among the start of the art.Yan ZhangXusheng GuJun TangKe ChengShoubiao TanIEEEarticleDeep learningreIDlayered partition strategyperson attribute recognitionsemantic featureElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 53585-53595 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning reID layered partition strategy person attribute recognition semantic feature Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Deep learning reID layered partition strategy person attribute recognition semantic feature Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yan Zhang Xusheng Gu Jun Tang Ke Cheng Shoubiao Tan Part-Based Attribute-Aware Network for Person Re-Identification
description	Despite the rapid progress over the past decade, person re-identification (reID) remains a challenging task due to the fact that discriminative features underlying different granularities are easily affected by illumination and camera-view variation. Most deep learning-based algorithms for reID extract global embedding as the representation of the pedestrian from the convolutional neural network. Considering that person attributes are robust and informative to identify pedestrians. This paper proposes a multi-branch model, namely part-based attribute-aware network (PAAN), to leverage both person reID and attribute performance, which not only utilizes ID label visible to the whole image but also utilizes attribute information. In order to learn discriminative and robust global representation which is invariant to the fact mentioned above, we resort to global and local person attributes to build global and local representation, respectively, utilizing our proposed layered partition strategy. Our goal is to exploit global or local semantic information to guide the optimization of global representation. Besides, in order to enhance the global representation, we design a semantic bridge replenishing mid-level semantic information for the final representation, which contains high-level semantic information. The extensive experiments are conducted to demonstrate the effectiveness of our proposed approach on two large-scale person re-identification datasets including Market-1501 and DukeMTMC-reID, and our approach achieves rank-1 of 92.40% on Market-1501 and 82.59% on DukeMTMC-reID showing strong competitiveness among the start of the art.
format	article
author	Yan Zhang Xusheng Gu Jun Tang Ke Cheng Shoubiao Tan
author_facet	Yan Zhang Xusheng Gu Jun Tang Ke Cheng Shoubiao Tan
author_sort	Yan Zhang
title	Part-Based Attribute-Aware Network for Person Re-Identification
title_short	Part-Based Attribute-Aware Network for Person Re-Identification
title_full	Part-Based Attribute-Aware Network for Person Re-Identification
title_fullStr	Part-Based Attribute-Aware Network for Person Re-Identification
title_full_unstemmed	Part-Based Attribute-Aware Network for Person Re-Identification
title_sort	part-based attribute-aware network for person re-identification
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/fdb14800318c4dadb21c26d14b9fbd85
work_keys_str_mv	AT yanzhang partbasedattributeawarenetworkforpersonreidentification AT xushenggu partbasedattributeawarenetworkforpersonreidentification AT juntang partbasedattributeawarenetworkforpersonreidentification AT kecheng partbasedattributeawarenetworkforpersonreidentification AT shoubiaotan partbasedattributeawarenetworkforpersonreidentification
_version_	1718420705664565248

Part-Based Attribute-Aware Network for Person Re-Identification

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