Article
Social Distancing is one of the main weapons in the fight against the coronavirus – and computer scientists have used a database of public cameras to keep track of how well we are following the guidelines.
Since April, Isha Ghodgaonkar at Purdue University, Indiana, and her colleagues have gathered around 0.5 terabytes of data per week from 11,140 public cameras connected to the internet.
More than 10.4 million images from the webcams have been run through deep-learning neural networks that automatically detect objects and differentiate them from people. Bounding boxes are drawn around the.
The algorithms draw bounding boxes around people, then calculate their distance from one another and whether they are practicing social distancing.
These observations showed that such practices are being followed to a degree, with both crowd densities and distancing lower after authorities imposed lockdowns and higher when those restrictions were relaxed.
Purdue's Isha Ghodgaonkar thinks monitoring movements via cameras are more effective than using location-tracking data from Google and Apple, as "location tracking might be slightly biased