期刊
NEUROCOMPUTING
卷 73, 期 10-12, 页码 1906-1917出版社
ELSEVIER
DOI: 10.1016/j.neucom.2010.01.020
关键词
Extreme learning machine; Activity recognition; 3D dual-tree complex wavelet transform; Two-dimensional PCA; Video classification
资金
- Canada Research Chair
- AUTO 21 NCE
- NSERC
This paper introduces a novel recognition framework for human actions using hybrid features. The hybrid features consist of spatio-temporal and local static features extracted using motion-selectivity attribute of 3D dual-tree complex wavelet transform (3D DT-CWT) and affine SIFT local image detector, respectively. The proposed model offers two core advantages: (1) the framework is significantly faster than traditional approaches due to volumetric processing of images as a '3D box of data' instead of a frame by frame analysis, (2) rich representation of human actions in terms of reduction in artifacts in view of the promising properties of our recently designed full symmetry complex filter banks with better directionality and shift-invariance properties. No assumptions about scene background, location, objects of interest, or point of view information are made whereas bidirectional two-dimensional PCA (2D-PCA) is employed for dimensionality reduction which offers enhanced capabilities to preserve structure and correlation amongst neighborhood pixels of a video frame. (C) 2010 Elsevier B.V. All rights reserved.
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