V-JAUNE: A framework for joint action recognition and video summarization

Hussein, F; Piccardi, M

V-JAUNE: A framework for joint action recognition and video summarization

Hussein, F Piccardi, M

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Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2017, 13 (2)
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Hussein, F	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2017, 13 (2)	en_US
dc.identifier.issn	1551-6857	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117675
dc.description.abstract	© 2017 ACM. Video summarization and action recognition are two important areas of multimedia video analysis. While these two areas have been tackled separately to date, in this article, we present a latent structural SVM framework to recognize the action and derive the summary of a video in a joint, simultaneous fashion. Efficient inference is provided by a submodular score function that accounts for the action and summary jointly. In this article, we also define a novel measure to evaluate the quality of a predicted video summary against the annotations of multiple annotators. Quantitative and qualitative results over two challenging action datasets-the ACE and MSR DailyActivity3D datasets-show that the proposed joint approach leads to higher action recognition accuracy and equivalent or better summary quality than comparable approaches that perform these tasks separately.	en_US
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications	en_US
dc.relation.isbasedon	10.1145/3063532	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	V-JAUNE: A framework for joint action recognition and video summarization	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	13	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© 2017 ACM. Video summarization and action recognition are two important areas of multimedia video analysis. While these two areas have been tackled separately to date, in this article, we present a latent structural SVM framework to recognize the action and derive the summary of a video in a joint, simultaneous fashion. Efficient inference is provided by a submodular score function that accounts for the action and summary jointly. In this article, we also define a novel measure to evaluate the quality of a predicted video summary against the annotations of multiple annotators. Quantitative and qualitative results over two challenging action datasets-the ACE and MSR DailyActivity3D datasets-show that the proposed joint approach leads to higher action recognition accuracy and equivalent or better summary quality than comparable approaches that perform these tasks separately.

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http://hdl.handle.net/10453/117675