
Overview
- Presents a comprehensive overview of the state of the art in feature representation and machine learning algorithms for action recognition from depth sensors
- Provides in-depth descriptions of novel feature representations and machine learning techniques
- Covers lower-level depth and skeleton features, higher-level representations to model temporal structure and human-object interactions, and feature selection techniques for occlusion handling
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (4 chapters)
Reviews
“It is a relatively short but self-contained volume that presents recent advances in the popular research area of human action recognition. … I was quite pleased when the student, to whom I passed the book for a through read, told me at the end that he found it very useful and a good start for his research. ... book is a good read for someone with an existing background in depth camera technology and research about human action recognition.” (Nicola Bellotto, IAPR Newsletter, Vol. 37 (2), 2015)
Authors and Affiliations
Bibliographic Information
Book Title: Human Action Recognition with Depth Cameras
Authors: Jiang Wang, Zicheng Liu, Ying Wu
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-04561-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-3-319-04560-3Published: 04 February 2014
eBook ISBN: 978-3-319-04561-0Published: 25 January 2014
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VIII, 59
Number of Illustrations: 23 b/w illustrations, 9 illustrations in colour
Topics: Image Processing and Computer Vision, Biometrics, User Interfaces and Human Computer Interaction