Single Image Human Proxemics Estimation for Visual Social Distancing
arXiv:2011.02018
Abstract
In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-automatic solution to approximate the homography matrix between the scene ground and image plane. With the estimated homography, we then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances using the length of their body-parts. Inter-personal distances are further locally inspected to detect possible violations of the social distancing rules. We validate our proposed method quantitatively and qualitatively against baselines on public domain datasets for which we provided groundtruth on inter-personal distances. Besides, we demonstrate the application of our method deployed in a real testing scenario where statistics on the inter-personal distances are currently used to improve the safety in a critical environment.
Paper accepted at WACV 2021 conference
References in corpus (7)
- VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
- A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
- Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
- LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images
- Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
- A Geometric Approach to Obtain a Bird's Eye View from an Image
- PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression