Human action recognition based on pyramid histogram of oriented gradients

Wang, Jin, Liu, Ping, She, Mary F. H., Kouzani, Abbas and Nahavandi, Saeid 2011, Human action recognition based on pyramid histogram of oriented gradients, in SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N. J., pp. 2449-2454.

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Title Human action recognition based on pyramid histogram of oriented gradients
Author(s) Wang, Jin
Liu, Ping
She, Mary F. H.ORCID iD for She, Mary F. H. orcid.org/0000-0001-8191-0820
Kouzani, AbbasORCID iD for Kouzani, Abbas orcid.org/0000-0002-6292-1214
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference of Systems, Man, and Cybernetics (2011 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 9-12 Oct. 2011
Title of proceedings SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and Cybernetics
Editor(s) [Unknown]
Publication date 2011
Conference series IEEE International Conference of Systems, Man, and Cybernetics
Start page 2449
End page 2454
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) action recognition
pyramid HOG
HMM
CRF
Summary Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.
ISBN 1457706520
9781457706523
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30042252

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