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Poster: ParkMaster: Leveraging Edge Computing in Visual Analytics

Authors:

Giovanni PauAuthors Info & Claims

MobiCom '15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

Pages 257 - 259

https://doi.org/10.1145/2789168.2795174

Published: 07 September 2015 Publication History

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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.

References

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http://sfpark.org/.

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https://developers.google.com/maps/documentation/roads/.

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T. Fabian. An algorithm for parking lot occupation detection. In Computer Information Systems and Industrial Management Applications, 2008. CISIM'08. 7th, pages 165--170. IEEE, 2008.

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N. Kaempchen, U. Franke, and R. Ott. Stereo vision based pose estimation of parking lots using 3d vehicle models. In Intelligent Vehicle Symposium, 2002. IEEE, volume 2, pages 459--464. IEEE, 2002.

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S. Mathur, T. Jin, N. Kasturirangan, J. Chandrasekaran, W. Xue, M. Gruteser, and W. Trappe. Parknet: drive-by sensing of road-side parking statistics. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 123--136. ACM, 2010.

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D. C. Shoup. Cruising for parking. Transport Policy, 13(6):479--486, 2006.

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N. True. Vacant parking space detection in static images. University of California, San Diego, 2007.

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Cited By

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Nallola SAyyasamy V(2024)Twenty-five years of real-time surveillance video analytics: a bibliometric reviewMultimedia Tools and Applications10.1007/s11042-024-18325-683:27(69273-69306)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18325-6
Kumari VReddy PSudhakar C(2022)Performance Interpretation of Machine Learning Based Classifiers for e-HealthCare System in Fog Computing Network2022 IEEE Students Conference on Engineering and Systems (SCES)10.1109/SCES55490.2022.9887698(01-05)Online publication date: 1-Jul-2022
https://doi.org/10.1109/SCES55490.2022.9887698
Cui LYang SChen FMing ZLu NQin J(2018)A survey on application of machine learning for Internet of ThingsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-018-0834-59:8(1399-1417)Online publication date: 11-Jun-2018
https://doi.org/10.1007/s13042-018-0834-5
Show More Cited By

Index Terms

Poster: ParkMaster: Leveraging Edge Computing in Visual Analytics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Published In

MobiCom '15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

September 2015

638 pages

ISBN:9781450336192

DOI:10.1145/2789168

General Chairs:
Serge Fdida
UPMC/LIP6, France
,
Giovanni Pau
UPMC/LIP6, France
,
Program Chairs:
Sneha Kumar Kasera
University of Utah, USA
,
Heather Zheng
University of California, Santa Barbara, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 September 2015

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MobiCom'15

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SIGMOBILE

MobiCom'15: The 21th Annual International Conference on Mobile Computing and Networking

September 7 - 11, 2015

Paris, France

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MobiCom '15 Paper Acceptance Rate 38 of 207 submissions, 18%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Nallola SAyyasamy V(2024)Twenty-five years of real-time surveillance video analytics: a bibliometric reviewMultimedia Tools and Applications10.1007/s11042-024-18325-683:27(69273-69306)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18325-6
Kumari VReddy PSudhakar C(2022)Performance Interpretation of Machine Learning Based Classifiers for e-HealthCare System in Fog Computing Network2022 IEEE Students Conference on Engineering and Systems (SCES)10.1109/SCES55490.2022.9887698(01-05)Online publication date: 1-Jul-2022
https://doi.org/10.1109/SCES55490.2022.9887698
Cui LYang SChen FMing ZLu NQin J(2018)A survey on application of machine learning for Internet of ThingsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-018-0834-59:8(1399-1417)Online publication date: 11-Jun-2018
https://doi.org/10.1007/s13042-018-0834-5
Lay-Ekuakille AGriffo GFazio MVillari M(2017)A Distributed Edge Computing Architecture to Support Sensing and Detecting Leaks in Waterworks Based on Advanced FDMIEEE Sensors Journal10.1109/JSEN.2017.272798417:23(7820-7827)Online publication date: 1-Dec-2017
https://doi.org/10.1109/JSEN.2017.2727984

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Recommendations

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

All one needs to know about fog computing and related edge computing paradigms: A complete survey

EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing

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