Human action analysis with randomized trees [electronic resource] / Gang Yu, Junsong Yuan, Zicheng Liu.
Yu, Gang, author.; Yuan, Junsong, author.; Liu, Zicheng, 1965- author.
2015
QA166.2 .Y8 2015eb
Available Online

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Title Human action analysis with randomized trees [electronic resource] / Gang Yu, Junsong Yuan, Zicheng Liu.
Author Yu, Gang, author.
ISBN 9789812871671 (electronic book)
9812871675 (electronic book)
9812871667
9789812871664
DOI https://doi.org/10.1007/978-981-287-167-1
Published Singapore : Springer Verlag, [2015]
Copyright ©2015
Language English
Description 1 online resource : color illustrations.
Other Standard Identifiers 10.1007/978-981-287-167-1 doi
Call Number QA166.2 .Y8 2015eb
Dewey Decimal Classification 511.52
Summary This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction. We will also describe techniques for space-time action localization including branch-and-bound sub-volume search and propagative Hough voting.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed Aug. 19, 2014).
Added Author Yuan, Junsong, author.
Liu, Zicheng, 1965- author.
Series SpringerBriefs in electrical and computer engineering. Signal processing.
Available in Other Form Print version: 9789812871664
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction to Human Action Analysis
Supervised Trees for Human Action Recognition and Detection
Unsupervised Trees for Human Action Search
Propagative Hough Voting to Leverage Contextual Information
Human Action Prediction with Multi-class Balanced Random Forest
Conclusion.

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