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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2017
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-981-10-4539-4 http://cds.cern.ch/record/2262146 |
_version_ | 1780954089227550720 |
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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 |
record_format | invenio |
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 |