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Human activity recognition and predi...

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Human activity recognition and prediction

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Human activity recognition and prediction/ edited by Yun Fu.
other author:	Fu, Yun.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	vii, 174 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-27004-3
ISBN:	9783319270043

Human activity recognition and prediction
Human activity recognition and prediction[electronic resource] /edited by Yun Fu. - Cham :Springer International Publishing :2016. - vii, 174 p. :ill., digital ;24 cm.

Introduction -- Action and Activities -- Action Recognition and Human Interaction -- Multimodal Action Recognition -- RGB-D Action Recognition -- Actionlets and Activity Prediction -- Time Series Modeling for Activity Prediction -- RGB-D Action 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 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.

ISBN: 9783319270043

Standard No.: 10.1007/978-3-319-27004-3doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Human activity recognition and prediction
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020 $a 9783319270029 $q (paper)
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245 0 0 $a Human activity recognition and prediction $h [electronic resource] / $c edited by Yun Fu.
260 $a Cham : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
300 $a vii, 174 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Action and Activities -- Action Recognition and Human Interaction -- Multimodal Action Recognition -- RGB-D Action Recognition -- Actionlets and Activity Prediction -- Time Series Modeling for Activity Prediction -- RGB-D Action Prediction.
520 $a 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.
650 0 $a Machine learning. $3 561253
650 0 $a Human-computer interaction. $3 555546
650 1 4 $a Engineering. $3 561152
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Biometrics. $3 677095
700 1 $a Fu, Yun. $3 843593
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-27004-3
950 $a Engineering (Springer-11647)

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