Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the...

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Main Authors: Suresh, Sundaram, Venkatesh Babu, R., Savitha, R.
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/98664
http://hdl.handle.net/10220/13654
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-986642020-05-28T07:17:53Z Human action recognition using a fast learning fully complex-valued classifier Suresh, Sundaram Venkatesh Babu, R. Savitha, R. School of Computer Engineering DRNTU::Engineering::Computer science and engineering In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, “fast learning fully complex-valued neural (FLFCN) classifier” is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. 2013-09-24T07:18:16Z 2019-12-06T19:58:14Z 2013-09-24T07:18:16Z 2019-12-06T19:58:14Z 2012 2012 Journal Article Venkatesh Babu, R., Suresh, S., & Savitha, R. (2012). Human action recognition using a fast learning fully complex-valued classifier. Neurocomputing, 89, 202-212. https://hdl.handle.net/10356/98664 http://hdl.handle.net/10220/13654 10.1016/j.neucom.2012.03.003 en Neurocomputing
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Suresh, Sundaram
Venkatesh Babu, R.
Savitha, R.
Human action recognition using a fast learning fully complex-valued classifier
description In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, “fast learning fully complex-valued neural (FLFCN) classifier” is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Suresh, Sundaram
Venkatesh Babu, R.
Savitha, R.
format Article
author Suresh, Sundaram
Venkatesh Babu, R.
Savitha, R.
author_sort Suresh, Sundaram
title Human action recognition using a fast learning fully complex-valued classifier
title_short Human action recognition using a fast learning fully complex-valued classifier
title_full Human action recognition using a fast learning fully complex-valued classifier
title_fullStr Human action recognition using a fast learning fully complex-valued classifier
title_full_unstemmed Human action recognition using a fast learning fully complex-valued classifier
title_sort human action recognition using a fast learning fully complex-valued classifier
publishDate 2013
url https://hdl.handle.net/10356/98664
http://hdl.handle.net/10220/13654
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