BEV-Net : Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning

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

6 Scopus Citations
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Author(s)

  • Zhirui Dai
  • Yuepeng Jiang
  • Yi Li
  • Bo Liu
  • Nuno Vasconcelos

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages5381-5391
ISBN (electronic)978-1-6654-2812-5
Publication statusPublished - Oct 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Conference

Title18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
PlaceCanada
CityVirtual, Online
Period11 - 17 October 2021

Abstract

Social distancing, an essential public health measure to limit the spread of contagious diseases, has gained significant attention since the outbreak of the COVID-19 pandemic. In this work, the problem of visual social distancing compliance assessment in busy public areas, with wide field-of-view cameras, is considered. A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed. A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated. BEV-Net combines detection of head and feet locations, camera pose estimation, a differentiable homography module to map image into BEV coordinates, and geometric reasoning to produce a BEV map of the people locations in the scene. Experiments on complex crowded scenes demonstrate the power of the approach and show superior performance over baselines derived from methods in the literature. Applications of interest for public health decision makers are finally discussed. Datasets, code and pretrained models are publicly available at GitHub.

Citation Format(s)

BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning. / Dai, Zhirui; Jiang, Yuepeng; Li, Yi et al.
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021). Institute of Electrical and Electronics Engineers, Inc., 2021. p. 5381-5391 (Proceedings of the IEEE International Conference on Computer Vision).

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