Multi‐level features extraction network with gating mechanism for crowd counting

Abstract Crowd counting is still a practical and challenging problem owing to scale variations and information loss. Most existing methods based on the straightforward fusion of different features from a deep neural network seem to eliminate this limitation. However, these features are difficult to...

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Autores principales:	Xin Zeng, Qiang Guo, Haoran Duan, Yunpeng Wu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/0606cda287e344b8832e3ee0ca63100c
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id	oai:doaj.org-article:0606cda287e344b8832e3ee0ca63100c
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spelling	oai:doaj.org-article:0606cda287e344b8832e3ee0ca63100c2021-11-29T03:38:16ZMulti‐level features extraction network with gating mechanism for crowd counting1751-96671751-965910.1049/ipr2.12304https://doaj.org/article/0606cda287e344b8832e3ee0ca63100c2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12304https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract Crowd counting is still a practical and challenging problem owing to scale variations and information loss. Most existing methods based on the straightforward fusion of different features from a deep neural network seem to eliminate this limitation. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. Unlike previous works, a multi‐level features extraction network with gating mechanism for crowd counting is proposed. Specifically, a multi‐channel gated unit to adaptively extract features in different levels of the network is proposed, which can avoid interference from confusing information. To fully aggregate features via multi‐level fusion, multi‐level features extraction scheme is presented. The multi‐level features extraction network learns to fuse features from multiple levels and reduce false predictions. Extensive experiments and evaluations clearly illustrate that the proposed approach achieves state‐of‐the‐art counting performance against other methods on four mainstream crowd counting benchmarks.Xin ZengQiang GuoHaoran DuanYunpeng WuWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3534-3542 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Xin Zeng Qiang Guo Haoran Duan Yunpeng Wu Multi‐level features extraction network with gating mechanism for crowd counting
description	Abstract Crowd counting is still a practical and challenging problem owing to scale variations and information loss. Most existing methods based on the straightforward fusion of different features from a deep neural network seem to eliminate this limitation. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. Unlike previous works, a multi‐level features extraction network with gating mechanism for crowd counting is proposed. Specifically, a multi‐channel gated unit to adaptively extract features in different levels of the network is proposed, which can avoid interference from confusing information. To fully aggregate features via multi‐level fusion, multi‐level features extraction scheme is presented. The multi‐level features extraction network learns to fuse features from multiple levels and reduce false predictions. Extensive experiments and evaluations clearly illustrate that the proposed approach achieves state‐of‐the‐art counting performance against other methods on four mainstream crowd counting benchmarks.
format	article
author	Xin Zeng Qiang Guo Haoran Duan Yunpeng Wu
author_facet	Xin Zeng Qiang Guo Haoran Duan Yunpeng Wu
author_sort	Xin Zeng
title	Multi‐level features extraction network with gating mechanism for crowd counting
title_short	Multi‐level features extraction network with gating mechanism for crowd counting
title_full	Multi‐level features extraction network with gating mechanism for crowd counting
title_fullStr	Multi‐level features extraction network with gating mechanism for crowd counting
title_full_unstemmed	Multi‐level features extraction network with gating mechanism for crowd counting
title_sort	multi‐level features extraction network with gating mechanism for crowd counting
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/0606cda287e344b8832e3ee0ca63100c
work_keys_str_mv	AT xinzeng multilevelfeaturesextractionnetworkwithgatingmechanismforcrowdcounting AT qiangguo multilevelfeaturesextractionnetworkwithgatingmechanismforcrowdcounting AT haoranduan multilevelfeaturesextractionnetworkwithgatingmechanismforcrowdcounting AT yunpengwu multilevelfeaturesextractionnetworkwithgatingmechanismforcrowdcounting
_version_	1718407628298649600

Multi‐level features extraction network with gating mechanism for crowd counting

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