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Perceiving humans in the context of Intelligent Transportation Systems (ITS)often relies on multiple cameras or expensive LiDAR sensors. In this work, wepresent a new cost-effective vision-based method that perceives humans'locations in 3D and their body orientation from a single image. We address thechallenges related to the ill-posed monocular 3D tasks by proposing a neuralnetwork architecture that predicts confidence intervals in contrast to pointestimates. Our neural network estimates human 3D body locations and theirorientation with a measure of uncertainty. Our proposed solution (i) isprivacy-safe, (ii) works with any fixed or moving cameras, and (iii) does notrely on ground plane estimation. We demonstrate the performance of our methodwith respect to three applications: locating humans in 3D, detecting socialinteractions, and verifying the compliance of recent safety measures due to theCOVID-19 outbreak. We show that it is possible to rethink the concept of"social distancing" as a form of social interaction in contrast to a simplelocation-based rule. We publicly share the source code towards an open sciencemission.

Perceiving Humans: from Monocular 3D Localization to Social Distancing