Combining Clustering Algorithms to Extract Symmetric Clusters from Noisy Data, Applied to Parking Lots

Gyulai-Nagy Zoltán-Valentin

2024

Abstract

The paper presents an approach for detecting symmetrical clusters in noisy data, using parking space detections as a real-world example. The paper proposes a plug-and-play solution that uses camera systems to automatically detect parking spaces and provide metrics about availability and accuracy. The approach uses clustering algorithms and image detection for data acquisition and mapping, which can be easily adapted to any application that requires geometrical data extraction. The paper also presents the different phases involved in mapping parking spaces and the challenges that need to be addressed. Overall, the proposed approach can benefit both parking lot administrators and drivers by providing real-time information on available parking spaces and reducing emissions, fuel costs, traffic, and time spent searching for a spot.

Paper Citation

in Harvard Style

Zoltán-Valentin G. (2024). Combining Clustering Algorithms to Extract Symmetric Clusters from Noisy Data, Applied to Parking Lots. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 362-369. DOI: 10.5220/0012624400003687

in Bibtex Style

@conference{enase24,
author={Gyulai-Nagy Zoltán-Valentin},
title={Combining Clustering Algorithms to Extract Symmetric Clusters from Noisy Data, Applied to Parking Lots},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={362-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012624400003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Combining Clustering Algorithms to Extract Symmetric Clusters from Noisy Data, Applied to Parking Lots
SN - 978-989-758-696-5
AU - Zoltán-Valentin G.
PY - 2024
SP - 362
EP - 369
DO - 10.5220/0012624400003687
PB - SciTePress