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Related Experiment Video

Updated: May 14, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Resource-Efficient Design and Implementation of Real-Time Parking Monitoring System with Edge Device.

Jungyoon Kim1, Incheol Jeong1, Jungil Jung2

  • 1Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary

This study presents an efficient smart parking system using edge computing and a dual-trigger mechanism for real-time vehicle occupancy detection. The system optimizes resource usage and ensures accuracy in constrained environments.

Keywords:
dual-trigger systemedge deviceimage processingsmart parking systemvehicle occupancy detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Smart City Technology

Background:

  • Parking management systems face challenges with high costs and infrastructure needs.
  • Edge computing offers reduced latency and operational costs but faces limitations in device computational resources.
  • Real-time vehicle occupancy detection is essential for efficient parking management.

Purpose of the Study:

  • To develop a cost-effective, real-time vehicle occupancy detection system for edge devices.
  • To optimize computational efficiency and resource usage in smart parking systems.
  • To address the challenges of implementing parking management in resource-constrained environments.

Main Methods:

  • Utilized SSD-MobileNetv2 for vehicle occupancy detection on edge devices.
  • Implemented a dual-trigger mechanism combining periodic and parking space mask triggers.
  • Processed video streams from multiple IP cameras for real-time analysis.

Main Results:

  • The parking space mask trigger significantly reduced unnecessary AI model executions.
  • The dual-trigger mechanism ensured consistent updates, even with unstable network conditions.
  • SSD-MobileNetv2 achieved a frame processing time of 0.32s with an F1-score of 0.9848 over four months.

Conclusions:

  • The proposed system is suitable for real-time parking management in resource-constrained environments.
  • The smart parking system offers an economical, viable, and practical solution for smart cities.
  • Edge computing combined with optimized AI models enhances the efficiency of parking management.