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Geometric invariant features for the detection and analysis of vehicle

The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram...

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Detalles Bibliográficos
Autores principales: Anandhalli, Mallikarjun, Tanuja, A., Baligar, Pavana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9018969/
https://www.ncbi.nlm.nih.gov/pubmed/35463223
http://dx.doi.org/10.1007/s11042-022-12919-8
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author Anandhalli, Mallikarjun
Tanuja, A.
Baligar, Pavana
author_facet Anandhalli, Mallikarjun
Tanuja, A.
Baligar, Pavana
author_sort Anandhalli, Mallikarjun
collection PubMed
description The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time.
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spelling pubmed-90189692022-04-20 Geometric invariant features for the detection and analysis of vehicle Anandhalli, Mallikarjun Tanuja, A. Baligar, Pavana Multimed Tools Appl Article The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time. Springer US 2022-04-20 2022 /pmc/articles/PMC9018969/ /pubmed/35463223 http://dx.doi.org/10.1007/s11042-022-12919-8 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Anandhalli, Mallikarjun
Tanuja, A.
Baligar, Pavana
Geometric invariant features for the detection and analysis of vehicle
title Geometric invariant features for the detection and analysis of vehicle
title_full Geometric invariant features for the detection and analysis of vehicle
title_fullStr Geometric invariant features for the detection and analysis of vehicle
title_full_unstemmed Geometric invariant features for the detection and analysis of vehicle
title_short Geometric invariant features for the detection and analysis of vehicle
title_sort geometric invariant features for the detection and analysis of vehicle
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9018969/
https://www.ncbi.nlm.nih.gov/pubmed/35463223
http://dx.doi.org/10.1007/s11042-022-12919-8
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