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Human activity recognition and prediction

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discuss...

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Detalles Bibliográficos
Autor principal: Fu, Yun
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-27004-3
http://cds.cern.ch/record/2120214
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author Fu, Yun
author_facet Fu, Yun
author_sort Fu, Yun
collection CERN
description This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .
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institution Organización Europea para la Investigación Nuclear
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spelling cern-21202142021-04-21T19:56:01Zdoi:10.1007/978-3-319-27004-3http://cds.cern.ch/record/2120214engFu, YunHuman activity recognition and predictionEngineeringThis book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .Springeroai:cds.cern.ch:21202142016
spellingShingle Engineering
Fu, Yun
Human activity recognition and prediction
title Human activity recognition and prediction
title_full Human activity recognition and prediction
title_fullStr Human activity recognition and prediction
title_full_unstemmed Human activity recognition and prediction
title_short Human activity recognition and prediction
title_sort human activity recognition and prediction
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-27004-3
http://cds.cern.ch/record/2120214
work_keys_str_mv AT fuyun humanactivityrecognitionandprediction