A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices

Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system. In this study, the authors investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.2984197
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Ke, Ruimin
  • Zhuang, Yifan
  • Pu, Ziyuan
  • Wang, Yanbing
  • 0000-0002-3988-8356
• Publication Date: 2021-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 4962-4974
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Artificial intelligence; Cloud computing; Internet of things; Parking facilities; Surveillance; Wireless sensor networks
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01780888
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2021 3:59PM