Implementation of Smart Parking Solution by Image Analysis

Aleksejs Zacepins, Vitalijs Komasilovs, Armands Kviesis

2018

Abstract

Modern smart city concept implies various smart aspects including smart parking management. Searching for a free parking lot can be a challenging task, especially during major events, therefore automatic system, which will help drivers to find a free parking is very valuable. There are many intrusive and non-intrusive technologies available for smart parking development, but authors of this paper developed a system based on video processing and analysis. Authors developed Python application for real-time parking lot monitoring based on video analysis of public video stream. Five classifier models (Logistic Regression, Linear Support Vector Machine, Radial Basis Function Support Vector Machine, Decision Tree and Random Forest) were compared for parking lot occupancy detection. Logistic regression classifier showed better results and was chosen for real-time parking monitoring application. System shows good performance and correctly predicted parking lot occupancy almost in all test cases.

Paper Citation

in Harvard Style

Zacepins A., Komasilovs V. and Kviesis A. (2018). Implementation of Smart Parking Solution by Image Analysis.In - RESIST, ISBN , pages 0-0. DOI: 10.5220/0006629706660669

in Bibtex Style

@conference{resist18,
author={Aleksejs Zacepins and Vitalijs Komasilovs and Armands Kviesis},
title={Implementation of Smart Parking Solution by Image Analysis},
booktitle={ - RESIST,},
year={2018},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006629706660669},
isbn={},
}

in EndNote Style

TY - CONF

JO - - RESIST,
TI - Implementation of Smart Parking Solution by Image Analysis
SN -
AU - Zacepins A.
AU - Komasilovs V.
AU - Kviesis A.
PY - 2018
SP - 0
EP - 0
DO - 10.5220/0006629706660669