Boosting eigenActions: A new algorithm for human action categorization

Chang Liu*, Pong Chi YUEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a boosting EigenActions algorithm for human action categorization. In determining the EigenActions, a spatio-temporal information saliency is first calculatedfrom the video sequence by estimating pixel density function. Since human action can be approximated as a periodic motion, salient action unit, which is one cycle ofthe motion, is extracted and EigenActions are determined using principle component analysis. A human action classifier is developed by multi-class Adaboost algorithm. Weizmann human action database with ninety different human actions is used to evaluate our proposed algorithm. The recognition accuracy is 98.3%. A comparison with two latest methods on human action recognition is also reported.

Original languageEnglish
Title of host publication2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
DOIs
Publication statusPublished - 2008
Event2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 - Amsterdam, Netherlands
Duration: 17 Sep 200819 Sep 2008

Publication series

Name2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008

Conference

Conference2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
Country/TerritoryNetherlands
CityAmsterdam
Period17/09/0819/09/08

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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