ParkMaster: Leveraging Edge Computing in Visual Analytics

Abstract : In this work we propose ParkMaster, a low-cost crowdsourc-ing 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.
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Poster
MobiCom'15 - 21st Annual International Conference on Mobile Computing and Networking, Sep 2015, Paris, France. ACM, MobiCom '15 Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. Pages 257-259, pp.257-259, 2015, 〈10.1145/2789168.2795174〉
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https://hal.archives-ouvertes.fr/hal-01231828
Contributeur : Giulio Grassi <>
Soumis le : vendredi 20 novembre 2015 - 19:05:30
Dernière modification le : jeudi 21 mars 2019 - 13:07:40

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Giulio Grassi, Matteo Sammarco, Paramvir Bahl, Kyle Jamieson, Giovanni Pau. ParkMaster: Leveraging Edge Computing in Visual Analytics. MobiCom'15 - 21st Annual International Conference on Mobile Computing and Networking, Sep 2015, Paris, France. ACM, MobiCom '15 Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. Pages 257-259, pp.257-259, 2015, 〈10.1145/2789168.2795174〉. 〈hal-01231828〉

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