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Roadside video data analysis: deep learning

This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning...

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Detalles Bibliográficos
Autores principales: Verma, Brijesh, Zhang, Ligang, Stockwell, David
Lenguaje:eng
Publicado: Springer 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-981-10-4539-4
http://cds.cern.ch/record/2262146
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author Verma, Brijesh
Zhang, Ligang
Stockwell, David
author_facet Verma, Brijesh
Zhang, Ligang
Stockwell, David
author_sort Verma, Brijesh
collection CERN
description This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment.
id cern-2262146
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
publisher Springer
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spelling cern-22621462021-04-21T19:15:30Zdoi:10.1007/978-981-10-4539-4http://cds.cern.ch/record/2262146engVerma, BrijeshZhang, LigangStockwell, DavidRoadside video data analysis: deep learningEngineeringThis book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment.Springeroai:cds.cern.ch:22621462017
spellingShingle Engineering
Verma, Brijesh
Zhang, Ligang
Stockwell, David
Roadside video data analysis: deep learning
title Roadside video data analysis: deep learning
title_full Roadside video data analysis: deep learning
title_fullStr Roadside video data analysis: deep learning
title_full_unstemmed Roadside video data analysis: deep learning
title_short Roadside video data analysis: deep learning
title_sort roadside video data analysis: deep learning
topic Engineering
url https://dx.doi.org/10.1007/978-981-10-4539-4
http://cds.cern.ch/record/2262146
work_keys_str_mv AT vermabrijesh roadsidevideodataanalysisdeeplearning
AT zhangligang roadsidevideodataanalysisdeeplearning
AT stockwelldavid roadsidevideodataanalysisdeeplearning