<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In urban areas, efficient parking management is crucial for reducing traffic congestion and environmental impact. This research introduces a new view for making the parking management system that leverages the capabilities of the NVidia Jetson Nano Single Board Computer (SBC) and OpenCV for real-time detection and classification of parking slot occupancy. Unlike traditional systems that rely on intrusive sensors, our proposed solution employs non-intrusive Oriented Fast and Rotated Brief (ORB) key point detection techniques using video feeds. The system architecture integrates video stream processing, ORB via OpenCV, cloud-based data storage, and a Flask server for user notifications. The methodology prioritizes traditional computer vision methods optimized for the Jetson Nano’s CUDA cores, offering a computationally efficient alternative to deep learning approaches. Python’s versatility and MongoDB’s document-based storage are employed for backend development. Our system’s performance, evaluated using open datasets, demonstrates high accuracy, precision, recall, and F1 scores, underlining its effectiveness in real-world urban parking scenarios. This study not only presents a robust solution for parking management but also opens avenues for similar applications in traffic measurement and urban planning.</p>
</abstract>

In urban areas, efficient parking management is crucial for reducing traffic congestion and environmental impact. This research introduces a new view for making the parking management system that leverages the capabilities of the NVidia Jetson Nano Single Board Computer (SBC) and OpenCV for real-time detection and classification of parking slot occupancy. Unlike traditional systems that rely on intrusive sensors, our proposed solution employs non-intrusive Oriented Fast and Rotated Brief (ORB) key point detection techniques using video feeds. The system architecture integrates video stream processing, ORB via OpenCV, cloud-based data storage, and a Flask server for user notifications. The methodology prioritizes traditional computer vision methods optimized for the Jetson Nano’s CUDA cores, offering a computationally efficient alternative to deep learning approaches. Python’s versatility and MongoDB’s document-based storage are employed for backend development. Our system’s performance, evaluated using open datasets, demonstrates high accuracy, precision, recall, and F1 scores, underlining its effectiveness in real-world urban parking scenarios. This study not only presents a robust solution for parking management but also opens avenues for similar applications in traffic measurement and urban planning.

Parking Management System Based on Key Points Detection

Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Parking Management System Based on Key Points Detection"}

In urban areas, efficient parking management is crucial for reducing traffic congestion and environmental impact. This research introduces a new view for...

Parking Management System Based on Key Points Detection

Publicado en línea: 02 feb 2024

Páginas: 33 - 39

Recibido: 01 dic 2023

Aceptado: 08 ene 2024

DOI: https://doi.org/10.2478/aei-2023-0015

Palabras clave
Internet of Things, Computer Vision, Key Points Detection, Edge Computing, Web Technologies

© 2023 Kristián Mičko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parking Management System Based on Key Points Detection

Publicado en línea: 02 feb 2024

Páginas: 33 - 39

Recibido: 01 dic 2023

Aceptado: 08 ene 2024

DOI: https://doi.org/10.2478/aei-2023-0015

Palabras claveInternet of Things, Computer Vision, Key Points Detection, Edge Computing, Web Technologies

© 2023 Kristián Mičko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Internet of Things, Computer Vision, Key Points Detection, Edge Computing, Web Technologies