Scale-Aware Rolling Fusion Network for Crowd Counting

Chen, Y; Gao, C; Su, Z; He, X; Liu, N

Scale-Aware Rolling Fusion Network for Crowd Counting

Chen, Y Gao, C Su, Z He, X

Liu, N

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2020, 2020-July
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Chen, Y
dc.contributor.author	Gao, C
dc.contributor.author	Su, Z
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Liu, N
dc.date	2020-07-06
dc.date.accessioned	2020-09-16T00:49:33Z
dc.date.available	2020-09-16T00:49:33Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2020, 2020-July
dc.identifier.isbn	9781728113319
dc.identifier.issn	1945-7871
dc.identifier.issn	1945-788X
dc.identifier.uri	http://hdl.handle.net/10453/142694
dc.description.abstract	© 2020 IEEE. Due to wide application prospects and various challenges such as large scale variation, inter-occlusion between crowd people and background noise, crowd counting is receiving increasing attention. In this paper, we propose a scale-aware rolling fusion network (SRF-Net) for crowd counting, which focuses on dealing with scale variation in highly congested noisy scenes. SRF-Net is a two-stage architecture that consists of a band-pass stage and a rolling guidance stage. Compared with the existing methods, SRF-Net achieves better results in retaining appropriate multi-level features and capturing multi-scale features, thus improving the quality of density estimation maps in crowded scenarios with large scale variation. We evaluate our method on three popular crowd counting datasets (ShanghaiTech, UCF-CC-50 and UCF-QNRF), and extensive experiments show its outperformance over the state-of-the-art approaches.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo
dc.relation.ispartof	2020 IEEE International Conference on Multimedia and Expo (ICME)
dc.relation.isbasedon	10.1109/ICME46284.2020.9102854
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Scale-Aware Rolling Fusion Network for Crowd Counting
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-09-16T00:49:12Z
pubs.finish-date	2020-07-10
pubs.publication-status	Published
pubs.start-date	2020-07-06
pubs.volume	2020-July

Abstract:

© 2020 IEEE. Due to wide application prospects and various challenges such as large scale variation, inter-occlusion between crowd people and background noise, crowd counting is receiving increasing attention. In this paper, we propose a scale-aware rolling fusion network (SRF-Net) for crowd counting, which focuses on dealing with scale variation in highly congested noisy scenes. SRF-Net is a two-stage architecture that consists of a band-pass stage and a rolling guidance stage. Compared with the existing methods, SRF-Net achieves better results in retaining appropriate multi-level features and capturing multi-scale features, thus improving the quality of density estimation maps in crowded scenarios with large scale variation. We evaluate our method on three popular crowd counting datasets (ShanghaiTech, UCF-CC-50 and UCF-QNRF), and extensive experiments show its outperformance over the state-of-the-art approaches.

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http://hdl.handle.net/10453/142694