DocumentCode :
594693
Title :
Human action recognition via affine moment invariants
Author :
Sadek, Sawsan ; Al-Hamadi, Ayoub ; Michaelis, B. ; Sayed, U.
Author_Institution :
Inst. for Electron., Signal Process. & Commun. (IESK), Otto-von-Guericke-Univ. Magdeburg, Magdeburg, Germany
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
218
Lastpage :
221
Abstract :
Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using affine moment invariants. Affine moment invariants are derived from the 3D spatio-temporal action volume and the average image created from the 3D volume, and classified by an SVM classifier. On KTH dataset, the method achieves performance results that compare favorably with these of other contemporary approaches reported in literature.
Keywords :
image classification; image motion analysis; support vector machines; 3D spatio-temporal action volume; KTH dataset; SVM classifier; activity recognition; affine moment invariant; computationally-efficient shape descriptor; human action recognition; Feature extraction; Humans; Pattern recognition; Real-time systems; Shape; Support vector machines; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460111
Link To Document :
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