Residual Regression with Semantic Prior for Crowd Counting

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

90 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages4031-4040
ISBN (print)9781728132938
Publication statusPublished - Jun 2019

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2019-June
ISSN (Print)1063-6919
ISSN (electronic)2575-7075

Conference

Title32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)
PlaceUnited States
CityLong Beach
Period16 - 20 June 2019

Abstract

Crowd counting is a challenging task due to factors such as large variations in crowdedness and severe occlusions. Although recent deep learning based counting algorithms have achieved a great progress, the correlation knowledge among samples and the semantic prior have not yet been fully exploited. In this paper, a residual regression framework is proposed for crowd counting utilizing the correlation information among samples. By incorporating such information into our network, we discover that more intrinsic characteristics can be learned by the network which thus generalizes better to unseen scenarios. Besides, we show how to effectively leverage the semantic prior to improve the performance of crowd counting. We also observe that the adversarial loss can be used to improve the quality of predicted density maps, thus leading to an improvement in crowd counting. Experiments on public datasets demonstrate the effectiveness and generalization ability of the proposed method.

Research Area(s)

  • Scene Analysis and Understanding, Vision Applications and Systems

Citation Format(s)

Residual Regression with Semantic Prior for Crowd Counting. / Wan, Jia; Luo, Wenhan; Wu, Baoyuan et al.
Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019. Institute of Electrical and Electronics Engineers, Inc., 2019. p. 4031-4040 8954128 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2019-June).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review