Human action analysis with randomized trees [electronic resource] / Gang Yu, Junsong Yuan, Zicheng Liu.
2015
QA166.2 .Y8 2015eb
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Details
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
9812871675 (electronic book)
9812871667
9789812871664
Published
Singapore : Springer Verlag, [2015]
Copyright
©2015
Language
English
Description
1 online resource : color illustrations.
Item Number
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.
Liu, Zicheng, 1965- author.
Series
SpringerBriefs in electrical and computer engineering. Signal processing.
Available in Other Form
Print version: 9789812871664
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Table of Contents
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.
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.