Fully complex-valued ELM classifiers for human action recognition

Babu, Venkatesh R and Suresh, S (2011) Fully complex-valued ELM classifiers for human action recognition. In: The 2011 International Joint Conference on Neural Networks (IJCNN), July 31 2011-Aug. 5 2011, San Jose, CA, USA.

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Abstract

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Item Type:	Conference Proceedings
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE.
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	26 Apr 2013 06:40
Last Modified:	26 Apr 2013 06:40
URI:	http://eprints.iisc.ac.in/id/eprint/46266

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