Action recognition using local joints structure and histograms of 3D joints

Yan Liang, Wanxuan Lu, Wei Liang, Yucheng Wang

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

8 Citations (Scopus)

Abstract

In this paper, we present a method for human action recognition using local joints structure and histograms of 3D joints. Global features like histograms of 3D joints [12] ignore the local structure information of the human body joints, which is also essential for accurate action recognition. To address this problem, we propose a local joints structure feature as a complement, and combine both global and local features for posture description in our method. Then, linear discriminant analysis is used to reduce the feature dimension, and k-means clustering is utilized to generate codewords. Finally, these codewords are treated as discrete symbols for training hidden Markov models (HMMs) which are used for action recognition. Experimental results demonstrate that our method has better performance than other methods when testing on UTKinect-Action Dataset and MSR Action3D dataset.

Original languageEnglish
Title of host publicationProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-188
Number of pages4
ISBN (Electronic)9781479974344
DOIs
Publication statusPublished - 20 Jan 2015
Event10th International Conference on Computational Intelligence and Security, CIS 2014 - Kunming, Yunnan, China
Duration: 15 Nov 201416 Nov 2014

Publication series

NameProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014

Conference

Conference10th International Conference on Computational Intelligence and Security, CIS 2014
Country/TerritoryChina
CityKunming, Yunnan
Period15/11/1416/11/14

Keywords

  • Histograms of 3D joints
  • Human action recognition
  • Local joints structure
  • Posture representation

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