Single Image Human Proxemics Estimation for Visual Social Distancing
This work addresses visual social distancing estimation for public safety in unconstrained environments, but it is incremental as it builds on existing pose detection and homography methods.
The paper tackles the problem of estimating social distancing from a single uncalibrated image by approximating homography and using pose detection to measure inter-personal distances, validating it on public datasets with provided groundtruth and deploying it in a real testing scenario to improve safety.
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.