We present the design and implementation of ParkMaster, a system that leverages the ubiquitous smartphone to help drivers find parking spaces in the urban environment. ParkMaster estimates parking space availability using video gleaned from drivers\u2019 dash-mounted smartphones on the network\u2019s edge, uploading analytics about the street to the cloud in real time as participants drive. Novel lightweight parked-car localization algorithms enable the system to estimate each parked car\u2019s approximate location by fusing information from phone\u2019s camera,GPS, and inertial sensors, tracking and counting parked cars as they move through the driving car\u2019s camera frame of view. To visually calibrate the system, ParkMaster relies only on the size of well-known objects in the urban environment for on-the-go calibration. We implement and deploy ParkMaster on Android smartphones, uploading parking analytics to the Azure cloud. On-the-road experiments in three different environments comprising Los Angeles, Paris and an Italian village measure the end-to-end accuracy of the system\u2019s parking estimates (close to 90%) as well as the amount of cellular data usage the system requires (less than one megabyte per hour). Drill-down micro benchmarks then analyze the factors contributing to this end-to-end performance, as video resolution, vision algorithm parameters, and CPU resources.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present the design and implementation of ParkMaster, a system that leverages the ubiquitous smartphone to help drivers find parking spaces in the urban environment. ParkMaster estimates parking space availability using video gleaned from drivers\u2019 dash-mounted smartphones on the network\u2019s edge, uploading analytics about the street to the cloud in real time as participants drive. 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