Human action recognition using extreme learning machine via multiple types of features

Rashid Minhas; Aryaz Baradarani; Sepideh Seifzadeh; Q. M. Jonathan Wu

doi:10.1117/12.853031

28 April 2010 Human action recognition using extreme learning machine via multiple types of features

Rashid Minhas, Aryaz Baradarani, Sepideh Seifzadeh, Q. M. Jonathan Wu

Proceedings Volume 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010; 770808 (2010) https://doi.org/10.1117/12.853031
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

This paper introduces a human actions recognition framework based on multiple types of features. Taking the advantage of motion-selectivity property of 3D dual-tree complex wavelet transform (3D DT-CWT) and affine SIFT local image detector, firstly spatio-temporal and local static features are extracted. No assumptions of 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. The proposed technique is significantly faster than traditional methods due to volumetric processing of input video, and offers a rich representation of human actions in terms of reduction in artifacts. Experimental examples are given to illustrate the effectiveness of the approach.

Citation Download Citation

Rashid Minhas, Aryaz Baradarani, Sepideh Seifzadeh, and Q. M. Jonathan Wu "Human action recognition using extreme learning machine via multiple types of features", Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 770808 (28 April 2010); https://doi.org/10.1117/12.853031

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