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Human Activity Recognition and Prediction [electronic resource] / edited by Yun Fu.

Contributor(s): Fu, Yun [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: VII, 174 p. 70 illus., 6 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319270043.Subject(s): Engineering | Image processing | Biometrics (Biology) | Engineering | Signal, Image and Speech Processing | Image Processing and Computer Vision | BiometricsAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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. .

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