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Human action recognition based on pyramid histogram of oriented gradients
conference contribution
posted on 2011-01-01, 00:00 authored by Jin Wang, P Liu, Fenghua She, Abbas KouzaniAbbas Kouzani, Saeid NahavandiHuman 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.
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Event
IEEE International Conference of Systems, Man, and Cybernetics (2011 : Anchorage, Alaska)Pagination
2449 - 2454Publisher
IEEELocation
Anchorage, AlaskaPlace of publication
Piscataway, N. J.Start date
2011-10-09End date
2011-10-12ISBN-13
9781457706523ISBN-10
1457706520Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2011, IEEETitle of proceedings
SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and CyberneticsUsage metrics
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