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Action recognition using completed local binary patterns and multiple-class boosting classifier

  • Beihang University
  • University of Texas at Dallas
  • Southeast University, Nanjing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper, for the first time, introduces a multiple-class boosting scheme (MBS) to combine depth motion maps (DMMs) and completed local binary patterns (CLBP) for action recognition. DMMs derive from projecting depth frames onto three orthogonal Cartesian planes (front, side and top) and characterize the motion energy of an action, on which the CLBP features are further extracted. And then a new multi-class boosting method is used and leads to an effective decision-level classifier. Extensive experiments on the MSRAction3D and MSRGesture3D datasets indicate that the proposed MBS method achieves new state-of-the-art results.

Original languageEnglish
Title of host publicationProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages336-340
Number of pages5
ISBN (Electronic)9781479961009
DOIs
StatePublished - 7 Jun 2016
Event3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia
Duration: 3 Nov 20166 Nov 2016

Publication series

NameProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

Conference

Conference3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Country/TerritoryMalaysia
CityKuala Lumpur
Period3/11/166/11/16

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