Estimation of Free Space on Car Park Using Computer Vision Algorithms

Mateusz Bukowski , Marcin Luckner , Robert Kunicki

Abstract

A system for monitoring of vacant parking spots can save drivers a lot of time and costs. Other citizens can benefit from a reduction of pollutions too. In our work, we proposed the computer vision system that estimates free space in a car park. The system uses three separate estimation methods based on various approaches to the estimation issue. The free car park area is recognised on a video frame by as the broadest cohesive area, the largest group of pixels with similar colours, and background for parked cars. The raw results of the estimations are aggregated by a Multi-Layer Perceptron to obtain the final estimate. The test on real data from the City of Warsaw showed that the system reaches 95% accuracy. Moreover, the results were compared with the registers from the parking machines to estimate a gap between covered payment and the accurate number of parked cars.
Author Mateusz Bukowski (FMIS)
Mateusz Bukowski,,
- Faculty of Mathematics and Information Science
, Marcin Luckner (FMIS / DSMKP)
Marcin Luckner,,
- Department of Structural Methods for Knowledge Processing
, Robert Kunicki (FMIS) - [Digitalisation Department, City of Warsaw]
Robert Kunicki,,
- Faculty of Mathematics and Information Science
- Digitalisation Department, City of Warsaw
Pages316-325
Publication size in sheets0.5
Book Szewczyk Roman, Zieliński Cezary, Kaliczyńska Małgorzata (eds.): Automation 2019: Progress in Automation, Robotics and Measurement Techniques, Advances in Intelligent Systems and Computing, vol. 920, 2020, Springer International Publishing, ISBN 978-3-030-13272-9, [978-3-030-13273-6], 727 p., DOI:10.1007/978-3-030-13273-6
Keywords in PolishWidzenie maszynowe, Inteligentne miasto, Sieci neuronowe, Przetwarzanie obrazu
Keywords in EnglishComputer Vision, Smart City, Neural Networks, Video Processing
Abstract in PolishSystem do monituringu wolnych miejsc parkingowych używając widzenia maszynowego. Zastosowano trzy gólnie znane metody przetwarzania obrazu i autorskie rozwiązanie łączące te podejścia. System osiągną 95 procentową skuteczność na rzeczywistych danych.
DOIDOI:10.1007/978-3-030-13273-6_30
URL https://link.springer.com/chapter/10.1007/978-3-030-13273-6_30
Languageen angielski
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 28-11-2019, ChapterFromConference
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