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An OpenCV-Driven Smart Parking Framework

Author

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  • R. Narmatha
  • C. Jayapratha

Abstract

The rapid rise in urban vehicle ownership has created major parking challenges, including congestion, time loss, and pollution. Traditional systems often rely on costly, non-scalable sensors and lack real-time monitoring. This paper presents a Smart Parking System (SPS) that uses computer vision, machine learning, and IoT to provide a scalable, accurate, and cost-effective solution. Leveraging cameras and a Convolutional Neural Network (CNN), the system detects parking occupancy with over 95% accuracy and guides drivers to the nearest available spot using an optimized distance algorithm. The architecture includes image capture, real-time processing via OpenCV, cloud storage, a mobile app, and an admin dashboard. Results show improved efficiency, reduced search time, and lower emissions, making SPS a strong candidate for smart city applications. Future enhancements include demand prediction and support for multi-level parking structures.

Suggested Citation

  • R. Narmatha & C. Jayapratha, 2025. "An OpenCV-Driven Smart Parking Framework," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(4), pages 98-102, August.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i4:id:985
    DOI: 10.32628/IJSRST251259
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