Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection

Chang, X; Ma, Z; Lin, M; Yang, Y; Hauptmann, AG

Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection

Chang, X

Ma, Z Lin, M Yang, Y

Hauptmann, AG

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26 (8), pp. 3911 - 3920
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Lin, M	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2017-08-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26 (8), pp. 3911 - 3920	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124718
dc.description.abstract	© 2017 IEEE. The Kinect sensing devices have been widely used in current Human-Computer Interaction entertainment. A fundamental issue involved is to detect users' motions accurately and quickly. In this paper, we tackle it by proposing a linear algorithm, which is augmented by feature interaction. The linear property guarantees its speed whereas feature interaction captures the higher order effect from the data to enhance its accuracy. The Schatten-p norm is leveraged to integrate the main linear effect and the higher order nonlinear effect by mining the correlation between them. The resulted classification model is a desirable combination of speed and accuracy. We propose a novel solution to solve our objective function. Experiments are performed on three public Kinect-based entertainment data sets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2017.2708506	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 IEEE. The Kinect sensing devices have been widely used in current Human-Computer Interaction entertainment. A fundamental issue involved is to detect users' motions accurately and quickly. In this paper, we tackle it by proposing a linear algorithm, which is augmented by feature interaction. The linear property guarantees its speed whereas feature interaction captures the higher order effect from the data to enhance its accuracy. The Schatten-p norm is leveraged to integrate the main linear effect and the higher order nonlinear effect by mining the correlation between them. The resulted classification model is a desirable combination of speed and accuracy. We propose a novel solution to solve our objective function. Experiments are performed on three public Kinect-based entertainment data sets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.

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http://hdl.handle.net/10453/124718