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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151662" target="_blank" >RIV/00216305:26220/24:PU151662 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10577808" target="_blank" >https://ieeexplore.ieee.org/document/10577808</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MECO62516.2024.10577808" target="_blank" >10.1109/MECO62516.2024.10577808</a>

Result language
angličtina
Original language name
Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study
Original language description
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.
Czech name
—
Czech description
—

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 13th Mediterranean Conference on Embedded Computing (MECO 2024)
ISBN
979-8-3503-8756-8
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
„“-„“
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
neuveden
Event location
Budva
Event date
Jun 11, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001268606200023

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