Overview
- Covers the most state-of-the-art topics of activity recognition and prediction
- Discusses both methodology and real-world practice of human activity recognition
- Contains contributions from top experts in the field, who voice their unique perspectives included throughout
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About this book
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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Keywords
Table of contents (8 chapters)
Editors and Affiliations
Bibliographic Information
Book Title: Human Activity Recognition and Prediction
Editors: Yun Fu
DOI: https://doi.org/10.1007/978-3-319-27004-3
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-27002-9Published: 06 January 2016
Softcover ISBN: 978-3-319-80055-4Published: 30 March 2018
eBook ISBN: 978-3-319-27004-3Published: 23 December 2015
Edition Number: 1
Number of Pages: VII, 174
Number of Illustrations: 6 b/w illustrations, 64 illustrations in colour
Topics: Signal, Image and Speech Processing, Image Processing and Computer Vision, Biometrics