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ParkMaster: Leveraging Edge Computing in Visual Analytics

Author(s): Grassi, Giulio; Sammarco, Matteo; Bahl, Paramvir; Jamieson, Kyle; Pau, Giovanni

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dc.contributor.authorGrassi, Giulio-
dc.contributor.authorSammarco, Matteo-
dc.contributor.authorBahl, Paramvir-
dc.contributor.authorJamieson, Kyle-
dc.contributor.authorPau, Giovanni-
dc.date.accessioned2021-10-08T19:49:43Z-
dc.date.available2021-10-08T19:49:43Z-
dc.date.issued2015-09en_US
dc.identifier.citationGrassi, Giulio, Matteo Sammarco, Paramvir Bahl, Kyle Jamieson, and Giovanni Pau. "Parkmaster: Leveraging edge computing in visual analytics." In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (2015): pp. 257-259. doi:10.1145/2789168.2795174en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1kr9n-
dc.description.abstractIn 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.en_US
dc.format.extent257 - 259en_US
dc.language.isoen_USen_US
dc.relation.ispartofProceedings of the 21st Annual International Conference on Mobile Computing and Networkingen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleParkMaster: Leveraging Edge Computing in Visual Analyticsen_US
dc.typeConference Articleen_US
dc.identifier.doi10.1145/2789168.2795174-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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