Object-Based Multiple Foreground Segmentation in RGBD Video

Fu, H; Xu, D; Lin, S

Object-Based Multiple Foreground Segmentation in RGBD Video

Fu, H Xu, D

Lin, S

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26 (3), pp. 1418 - 1427
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Fu, H	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Lin, S	en_US
dc.date.issued	2017-03-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26 (3), pp. 1418 - 1427	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126753
dc.description.abstract	© 1992-2012 IEEE. We present an RGB and Depth (RGBD) video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fully-connected graph, where a flexible number of foregrounds may be chosen. In our graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are selected based on an RGBD video saliency map in which depth-based features are utilized to enhance the identification of foregrounds. Experiments show that the proposed multiple foreground segmentation method outperforms related techniques, and the depth cue serves as a helpful complement to RGB features. Moreover, our method provides performance comparable to the state-of-The-Art RGB video segmentation techniques on regular RGB videos with estimated depth maps.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2017.2651369	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Object-Based Multiple Foreground Segmentation in RGBD Video	en_US
dc.type	Journal Article
utslib.citation.volume	3	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
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 1992-2012 IEEE. We present an RGB and Depth (RGBD) video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fully-connected graph, where a flexible number of foregrounds may be chosen. In our graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are selected based on an RGBD video saliency map in which depth-based features are utilized to enhance the identification of foregrounds. Experiments show that the proposed multiple foreground segmentation method outperforms related techniques, and the depth cue serves as a helpful complement to RGB features. Moreover, our method provides performance comparable to the state-of-The-Art RGB video segmentation techniques on regular RGB videos with estimated depth maps.

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http://hdl.handle.net/10453/126753