Social-Distancing | Code for estimating social distances from RGB cameras | Computer Vision library

Social-Distancing is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, OpenCV applications. Social-Distancing has no bugs, it has no vulnerabilities, it has build file available and it has low support. However Social-Distancing has a Non-SPDX License. You can download it from GitHub.

Given a frame captured from a scene, the algorithm first detects visible people in the scene using an off-the-shelf body pose detector and estimates the height of the people through measuring the distance from their body joints. In the second step, the algorithm estimates an area of one meter around all the detected people. This distance is roughly estimated proportional to a typical human body height of 160 cm and can be used to draw a circle centered in human position in the scene. In the third step, the Homography of the scene is estimated given two parameters which essentially map the rectangular bird’s view model for the scene to the trapezoidal perspective view of the scene. These two parameters need to be manually tuned to estimate best the scene perspective. According to the Homography matrix, the safe circular distance for each person is converted to ellipsoids in perspective view. The people are considered to be staying in safe distance from each other if their ellipsoids do not collide. Conversely, if ellipsoids of two people collide, those people are considered as being in risk and their ellipsoids will be shown in red. If you use this code as part of your research, please cite our work.