Predicting social distancing through Machine Learning
Over the past few months the world has experienced a series of Covid-19 outbreaks that have generally followed the same pathway: an initial phase with few infections and limited response, followed by a take-off of the famous epidemic curve accompanied by a country-wide lockdown to flatten the curve.
Using multiple sources of data, machine-learning models would be trained. The clinical risk predictions couldbe used to customize policies and resource allocation at the individual/household level, appropriately accounting for standard medical liabilities and risks. It could, for instance, enable us to target social distancing and protection for those with high clinical risk scores, while allowing those with low scores to live more or less normally. The criteria for assigning individuals to high or low risk groups would, of course, need to be determined, also considering available resources, medical liability risks, and other risk trade-offs, but the data science approaches for this are standard and used in numerous applications.
Nations have acquainted social distancing measures to slow down the spread of the COVID-19 pandemic. This involves when outside individuals should remain at least 2 meters (6 feet) away from one another consistently. While this is anything but a troublesome prerequisite to meet for the vast majority, laborers in businesses that have been recognized as fundamental and keep on working during this present time of quarantine won’t discover it as simple to meet this rule.
Stores and workplaces eager to avoid spreading the novel coronavirus are equipping existing security cameras with artificial intelligence software that can track compliance with health guidelines including social distancing and mask-wearing.
Companies have created a new workplace monitoring tool that issues an alert when anyone is less than the desired distance from a colleague. New social distancing detector has two parts, on the left is a feed of people walking around on the street. On the right, a birds eye diagram represents each one as a dot and turns them bright red when they move too close to someone else. The company says the tool is meant to be used in work settings like factory floors and was developed in response to the request of its customers. The detector must first be calibrated to map any security footage against the real-world dimensions. A trained neural network then picks out the people in the video, and another algorithm computes the distances between them.
It additionally says the tool can undoubtedly be incorporated into existing surveillance camera systems, however, that it is as yet exploring how to advise individuals when they break social distancing. One potential strategy is an alert that sounds when laborers pass too near each other. A report could likewise be created overtime to assist supervisors with revamping the workspace, the organization says.
Using computer vision, people who do not maintain a certain distance at crowded places can be identified. The system keeps a log of videos that can be used to take action against violators. When two or more than two persons are found in close contact in the camera, an alarm alerts people standing in that area and notification is sent to concerned authorities or guards who can ask people to maintain distance
Crowded places like malls and supermarkets can integrate a plugin on their website or app to display the live count of people so that people can follow social distancing. It can display analytics on the store website about the crowd prediction and help customers book the free pickup slot.
Where can we use Social Distancing AI Apps
· Manufacturing plants
· Retail shops
· Metro Stations
· Public libraries
· Religious Places