This article analyzes how streams of people move around a city. Wi-Fi sniffing techniques and camera systems are used for classifying vehicles, people, bicycles and scooters. The system is able to detect the presence of people by sniffing the mac address of the smartphone's Wi-Fi radio interface and with the use of cameras to monitor the presence of people, vehicles, and other means of transport, classifying them through the contribution of neural networks. The proposed solution has a high level of reliability overcoming the limits of a single technological system that offers imprecise and inaccurate monitoring.
Machine Learning-based Urban Mobility Monitoring System / Bertolusso, M.; Spanu, M.; Popescu, V.; Fadda, M.; Giusto, D.. - (2022), pp. 747-748. (Intervento presentato al convegno 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 tenutosi a usa nel 2022) [10.1109/CCNC49033.2022.9700694].
Machine Learning-based Urban Mobility Monitoring System
Popescu V.;Fadda M.;Giusto D.
2022-01-01
Abstract
This article analyzes how streams of people move around a city. Wi-Fi sniffing techniques and camera systems are used for classifying vehicles, people, bicycles and scooters. The system is able to detect the presence of people by sniffing the mac address of the smartphone's Wi-Fi radio interface and with the use of cameras to monitor the presence of people, vehicles, and other means of transport, classifying them through the contribution of neural networks. The proposed solution has a high level of reliability overcoming the limits of a single technological system that offers imprecise and inaccurate monitoring.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
