MFP‐Net: Multi‐scale feature pyramid network for crowd counting

Abstract Although deep learning has been widely used for dense crowd counting, it still faces two challenges. Firstly, the popular network models are sensitive to scale variance of human head, human occlusions, and complex background due to repeated utilization of vanilla convolution kernels. Second...

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Autores principales:	Tao Lei, Dong Zhang, Risheng Wang, Shuying Li, Weijiang Zhang, Asoke K. Nandi
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/8b12265dc6084314af4d3093b0140ab0
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spelling	oai:doaj.org-article:8b12265dc6084314af4d3093b0140ab02021-11-29T03:38:16ZMFP‐Net: Multi‐scale feature pyramid network for crowd counting1751-96671751-965910.1049/ipr2.12230https://doaj.org/article/8b12265dc6084314af4d3093b0140ab02021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12230https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract Although deep learning has been widely used for dense crowd counting, it still faces two challenges. Firstly, the popular network models are sensitive to scale variance of human head, human occlusions, and complex background due to repeated utilization of vanilla convolution kernels. Secondly, the vanilla feature fusion often depends on summation or concatenation, which ignores the correlation of different features leading to information redundancy and low robustness to background noise. To address these issues, a multi‐scale feature pyramid network (MFP‐Net) for dense crowd counting is proposed in this paper. The proposed MFP‐Net makes two contributions. Firstly, the feature pyramid fusion module is designed that adopts rich convolutions with different depths and scales, not only to expand the receptive field, but also to improve the inference speed of models by using parallel group convolution. Secondly, a feature attention‐aware module is added in the feature fusion stage. The module can achieve local and global information fusion by capturing the importance of the spatial and channel domains to improve model robustness. The proposed MFP‐Net is evaluated on five publicly available datasets, and experiments show that the MFP‐Net not only provides better crowd counting results than comparative models, but also requires fewer parameters.Tao LeiDong ZhangRisheng WangShuying LiWeijiang ZhangAsoke K. NandiWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3522-3533 (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 Tao Lei Dong Zhang Risheng Wang Shuying Li Weijiang Zhang Asoke K. Nandi MFP‐Net: Multi‐scale feature pyramid network for crowd counting
description	Abstract Although deep learning has been widely used for dense crowd counting, it still faces two challenges. Firstly, the popular network models are sensitive to scale variance of human head, human occlusions, and complex background due to repeated utilization of vanilla convolution kernels. Secondly, the vanilla feature fusion often depends on summation or concatenation, which ignores the correlation of different features leading to information redundancy and low robustness to background noise. To address these issues, a multi‐scale feature pyramid network (MFP‐Net) for dense crowd counting is proposed in this paper. The proposed MFP‐Net makes two contributions. Firstly, the feature pyramid fusion module is designed that adopts rich convolutions with different depths and scales, not only to expand the receptive field, but also to improve the inference speed of models by using parallel group convolution. Secondly, a feature attention‐aware module is added in the feature fusion stage. The module can achieve local and global information fusion by capturing the importance of the spatial and channel domains to improve model robustness. The proposed MFP‐Net is evaluated on five publicly available datasets, and experiments show that the MFP‐Net not only provides better crowd counting results than comparative models, but also requires fewer parameters.
format	article
author	Tao Lei Dong Zhang Risheng Wang Shuying Li Weijiang Zhang Asoke K. Nandi
author_facet	Tao Lei Dong Zhang Risheng Wang Shuying Li Weijiang Zhang Asoke K. Nandi
author_sort	Tao Lei
title	MFP‐Net: Multi‐scale feature pyramid network for crowd counting
title_short	MFP‐Net: Multi‐scale feature pyramid network for crowd counting
title_full	MFP‐Net: Multi‐scale feature pyramid network for crowd counting
title_fullStr	MFP‐Net: Multi‐scale feature pyramid network for crowd counting
title_full_unstemmed	MFP‐Net: Multi‐scale feature pyramid network for crowd counting
title_sort	mfp‐net: multi‐scale feature pyramid network for crowd counting
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/8b12265dc6084314af4d3093b0140ab0
work_keys_str_mv	AT taolei mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting AT dongzhang mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting AT rishengwang mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting AT shuyingli mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting AT weijiangzhang mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting AT asokeknandi mfpnetmultiscalefeaturepyramidnetworkforcrowdcounting
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MFP‐Net: Multi‐scale feature pyramid network for crowd counting

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