CrowdVisor: an embedded toolset for human activity monitoring in critical environments

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

Smart City  Deep Learning  Computer vision  Embedded systems 

As evidenced during the recent COVID-19 pandemic, there are scenarios in which ensuring compliance to a set of guidelines (such as wearing medical masks and keeping a certain physical distance among people) becomes crucial to secure a safe living environment. However, human supervision could not always guarantee this task, especially in crowded scenes. This abstract presents CrowdVisor, an embedded modular Computer Vision-based and AI-assisted system that can carry out several tasks to help monitor individual and collective human safety rules. We strive for a real-time but low-cost system, thus complying with the compute- and storage-limited resources availability typical of off-the-shelves embedded devices, where images are captured and processed directly onboard. Our solution consists of multiple modules relying on well-researched neural network components, each responsible for specific functionalities that the user can easily enable and configure. In particular, by exploiting one of these modules or combining some of them, our framework makes available many capabilities. They range from the ability to estimate the so-called social distance to the estimation of the number of people present in the monitored scene, as well as the possibility to localize and classify Personal Protective Equipment (PPE) worn by people (such as helmets and face masks). To validate our solution, we test all the functionalities that our framework makes available over two novel datasets that we collected and annotated on purpose. Experiments show that our system provides a valuable asset to monitor compliance with safety rules automatically.

Source: I-CiTies 2022 - 8th Italian Conference on ICT for Smart Cities And Communities, Ascoli Piceno, Italy, 14-16/09/2022