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Human Action Analysis with Randomized Trees

  • Book
  • © 2015

Overview

Authors:
  1. Gang Yu

    1. School of Electrical and Electronic Eng., Nanyang Technological University, Singapore, Singapore

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  2. Junsong Yuan

    1. School of Electrical and Electronic Eng., Nanyang Technological University, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  3. Zicheng Liu

    1. Microsoft Research, Redmond, USA

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  • 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
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

Part of the book sub series: SpringerBriefs in Signal Processing (BRIEFSSIGNAL)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction to Human Action Analysis

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 1-8

  3. Supervised Trees for Human Action Recognition and Detection

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 9-27

  4. Unsupervised Trees for Human Action Search

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 29-56

  5. Propagative Hough Voting to Leverage Contextual Information

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 57-72

  6. Human Action Prediction with Multiclass Balanced Random Forest

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 73-81

  7. Conclusion

    • Gang Yu, Junsong Yuan, Zicheng Liu
    Pages 83-83
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Keywords

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.

Authors and Affiliations

  • School of Electrical and Electronic Eng., Nanyang Technological University, Singapore, Singapore

    Gang Yu, Junsong Yuan

  • Microsoft Research, Redmond, USA

    Zicheng Liu

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