HMM-based human action recognition using multiview image sequences

Mohiuddin Ahmad, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

80 Citations (Scopus)

Abstract

In this paper, we present a novel method for human action recognition from any arbitrary view image sequence that uses the Cartesian component of optical flow velocity and human body silhouette feature vector information. We use principal component analysis (PCA) to reduce the higher dimensional silhouette feature space into lower dimensional feature space. The action region in an image frame represents Q-dimensional optical flow feature vector and R-dimensional silhouette feature vector. We represent each action using a set of hidden Markov models and we model each action for any viewing direction by using the combined (Q + R)-dimensional features at any instant of time. We perform experiments of the proposed method by using KU gesture database and manually captured data. Experimental results of different actions from any viewing direction are correctly classified by our method, which indicate the robustness of our view-independent method.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages263-266
Number of pages4
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Ahmad, M., & Lee, S. W. (2006). HMM-based human action recognition using multiview image sequences. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 263-266). [1698883] (Proceedings - International Conference on Pattern Recognition; Vol. 1). https://doi.org/10.1109/ICPR.2006.630