Parkmaster: an in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments.

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 driversu0027 dash-mounted smartphones on the networku0027s 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 caru0027s approximate location by fusing information from phoneu0027s camera, GPS, and inertial sensors, tracking and counting parked cars as they move through the driving caru0027s 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 systemu0027s parking estimates (close to 90%) as well as the amount of cellular data usage the system requires (less than one mega-byte per hour). Drill-down microbenchmarks then analyze the factors contributing to this end-to-end performance, as video resolution, vision algorithm parameters, and CPU resources.