An embedded toolset for human activity monitoring in critical environments

Di Benedetto M., Carrara F., Ciampi L., Falchi F., Gennaro C., Amato G.

Deep learning  Computer vision  Machine learning  Personal protective equipment  Counting  Homography  Embedded system 

In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.

Source: Expert systems with applications 199 (2022). doi:10.1016/j.eswa.2022.117125

Publisher: Pergamon,, Oxford , Regno Unito

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