In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.
ParkMaster - Leveraging edge computing in visual analytics / Grassi, Giulio; Sammarco, Matteo; Bahl, Paramvir; Jamieson, Kyle; Pau, Giovanni. - STAMPA. - 2015-:(2015), pp. 257-259. (Intervento presentato al convegno 21st Annual International Conference on Mobile Computing and Networking, MobiCom 2015 tenutosi a fra nel 2015) [10.1145/2789168.2795174].
ParkMaster - Leveraging edge computing in visual analytics
Pau, Giovanni
2015
Abstract
In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
