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Application of Template Matching for Improving Classification of Urban Railroad Point Clouds

This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are fo...

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Detalles Bibliográficos
Autores principales: Arastounia, Mostafa, Oude Elberink, Sander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191092/
https://www.ncbi.nlm.nih.gov/pubmed/27973452
http://dx.doi.org/10.3390/s16122112
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author Arastounia, Mostafa
Oude Elberink, Sander
author_facet Arastounia, Mostafa
Oude Elberink, Sander
author_sort Arastounia, Mostafa
collection PubMed
description This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor.
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spelling pubmed-51910922017-01-03 Application of Template Matching for Improving Classification of Urban Railroad Point Clouds Arastounia, Mostafa Oude Elberink, Sander Sensors (Basel) Article This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor. MDPI 2016-12-12 /pmc/articles/PMC5191092/ /pubmed/27973452 http://dx.doi.org/10.3390/s16122112 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arastounia, Mostafa
Oude Elberink, Sander
Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title_full Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title_fullStr Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title_full_unstemmed Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title_short Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
title_sort application of template matching for improving classification of urban railroad point clouds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191092/
https://www.ncbi.nlm.nih.gov/pubmed/27973452
http://dx.doi.org/10.3390/s16122112
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