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This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed multi-camera system is capable of automatically estimating the number of cars present in the entire parking lot directly on board the edge devices. It comprises an on-device deep learning-based detector that locates and counts the vehicles from the captured images and a decentralized geometric-based approach that can analyze the inter-camera shared areas and merge the data acquired by all the devices. We conducted the experimental evaluation on an extended version of the CNRPark-EXT dataset, a collection of images taken from the parking lot on the campus of the National Research Council (CNR) in Pisa, Italy. We show that our system is robust and takes advantage of the redundant information deriving from the different cameras, improving the overall performance without requiring any extra geometrical information of the monitored scene. • Smart mobility is crucial for smart cities and traffic-related issues. • We introduce a multi-camera system able to count cars from images of parking areas. • We combine a deep learning-based technique and a decentralized geometric approach. • All the algorithms run on the edge devices reducing the traffic on the network. • Our solution benefits from redundant information from different data sources. [ABSTRACT FROM AUTHOR]

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. • Monitoring compliance with safety rules is crucial in critical environments. • Presenting a Computer Vision-based AI-assisted system to monitor human activities. • Modular architecture for pedestrian detection, counting, distancing, and PPE detection. • Two novel datasets of PPE detection and overall system evaluation in a real scenario. • State-of-the-art trained models in a deployed real use-case scenario in Pisa, Italy. [ABSTRACT FROM AUTHOR]