BEV-Net : Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021) |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 5381-5391 |
ISBN (electronic) | 978-1-6654-2812-5 |
Publication status | Published - Oct 2021 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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ISSN (Print) | 1550-5499 |
Conference
Title | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
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Place | Canada |
City | Virtual, Online |
Period | 11 - 17 October 2021 |
Link(s)
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).
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 Works › RGC 32 - Refereed conference paper (with host publication) › peer-review