ParkMaster: An in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments

Giulio Grassi, Paramvir Bahl, Kyle Jamieson, Giovanni Pau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

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' dash-mounted smartphones on the network's 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's approximate location by fusing information from phone's camera, GPS, and inertial sensors, tracking and counting parked cars as they move through the driving car's 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's 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 microbenchmarks then analyze the factors contributing to this end-To-end performance, as video resolution, vision algorithm parameters, and CPU resources.

Original languageEnglish (US)
Title of host publication2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450350877
DOIs
StatePublished - Oct 12 2017
Event2nd IEEE/ACM Symposium on Edge Computing, SEC 2017 - San Jose, United States
Duration: Oct 12 2017Oct 14 2017

Publication series

Name2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017

Other

Other2nd IEEE/ACM Symposium on Edge Computing, SEC 2017
CountryUnited States
CitySan Jose
Period10/12/1710/14/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

Keywords

  • Edge computing
  • Fog computing
  • Mobile Systems
  • Visual Analytics

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  • Cite this

    Grassi, G., Bahl, P., Jamieson, K., & Pau, G. (2017). ParkMaster: An in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments. In 2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017 [a16] (2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3132211.3134452