To refer to this page use:
|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.|
|Citation:||Grassi, 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:10.1145/2789168.2795174|
|Pages:||257 - 259|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 21st Annual International Conference on Mobile Computing and Networking|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.