Scale-Aware Rolling Fusion Network for Crowd Counting

Ying Chen, Chengying Gao, Zhuo Su, Xiangjian He, Ning Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo, ICME 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728113319
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE International Conference on Multimedia and Expo, ICME 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2020 IEEE International Conference on Multimedia and Expo, ICME 2020
Country/TerritoryUnited Kingdom


  • Crowd Counting
  • Multi-Scale Feature
  • Regressvie Supervision
  • Rolling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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