SpringerBriefs in Signal Processing

Human Action Analysis with Randomized Trees

Authors: Yu, Gang, Yuan, Junsong, Liu, Zicheng

  • Step-by-step introduction to help the readers understand the topic of human action analysis
  • Presents one basic algorithm that can used in various applications
  • Practical examples and applications will be presented
  • Covers the most recent advancement in the human action analysis
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eBook $39.99
price for USA (gross)
  • ISBN 978-981-287-167-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-981-287-166-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
About this book

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.

Table of contents (6 chapters)

  • Introduction to Human Action Analysis

    Yu, Gang (et al.)

    Pages 1-8

  • Supervised Trees for Human Action Recognition and Detection

    Yu, Gang (et al.)

    Pages 9-27

  • Unsupervised Trees for Human Action Search

    Yu, Gang (et al.)

    Pages 29-56

  • Propagative Hough Voting to Leverage Contextual Information

    Yu, Gang (et al.)

    Pages 57-72

  • Human Action Prediction with Multiclass Balanced Random Forest

    Yu, Gang (et al.)

    Pages 73-81

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-981-287-167-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $54.99
price for USA
  • ISBN 978-981-287-166-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Human Action Analysis with Randomized Trees
Authors
Series Title
SpringerBriefs in Signal Processing
Copyright
2015
Publisher
Springer Singapore
Copyright Holder
The Author(s)
eBook ISBN
978-981-287-167-1
DOI
10.1007/978-981-287-167-1
Softcover ISBN
978-981-287-166-4
Series ISSN
2196-4076
Edition Number
1
Number of Pages
VIII, 83
Number of Illustrations and Tables
30 illustrations in colour
Topics