Publication detail

Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study

FRÝZA, T. KUŽELA, M. ZELENÝ, O.

Original Title

Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study

Type

conference paper

Language

English

Original Abstract

This paper addresses the challenges associated with urban mobility and introduces a~low-complexity system for detecting parking lot occupancy using machine learning and computer vision techniques. Through a~field experiment at a~Czech university, images of parking areas were captured to create a~dataset titled T10Lot, and classified to get parking spot occupancy using Raspberry Pi computer. Results indicate satisfactory accuracy despite challenges such as varying lighting conditions and weather.

Keywords

Machine learning, smart parking, edge device, classifier, IoT

Authors

FRÝZA, T.; KUŽELA, M.; ZELENÝ, O.

Released

11. 6. 2024

Publisher

Institute of Electrical and Electronics Engineers Inc.

ISBN

979-8-3503-8756-8

Book

Proceedings of 13th Mediterranean Conference on Embedded Computing (MECO 2024)

Pages count

4

URL

https://ieeexplore.ieee.org/document/10577808

BibTex

@inproceedings{BUT189015,
  author="Tomáš {Frýza} and Miloslav {Kužela} and Ondřej {Zelený}",
  title="Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study",
  booktitle="Proceedings of 13th Mediterranean Conference on Embedded Computing (MECO 2024)",
  year="2024",
  pages="4",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  doi="10.1109/MECO62516.2024.10577808",
  isbn="979-8-3503-8756-8",
  url="https://ieeexplore.ieee.org/document/10577808"
}