Exploiting detected visual objects for frame-level video filtering

Du, X; Yin, H; Huang, Z; Yang, Y; Zhou, X

Exploiting detected visual objects for frame-level video filtering

Du, X Yin, H Huang, Z Yang, Y

Zhou, X

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Publication Type:: Journal Article
Citation:: World Wide Web, 2018, 21 (5), pp. 1259 - 1284
Issue Date:: 2018-09-01

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	Filename	Description	Size
	10.1007%2Fs11280-017-0505-6.pdf	Published Version	2.7 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Du, X	en_US
dc.contributor.author	Yin, H	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	World Wide Web, 2018, 21 (5), pp. 1259 - 1284	en_US
dc.identifier.issn	1386-145X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124717
dc.description.abstract	© 2017, Springer Science+Business Media, LLC. Videos are generated at an unprecedented speed on the web. To improve the efficiency of access, developing new ways to filter the videos becomes a popular research topic. One on-going direction is using visual objects to perform frame-level video filtering. Under this direction, existing works create the unique object table and the occurrence table to maintain the connections between videos and objects. However, the creation process is not scalable and dynamic because it heavily depends on human labeling. To improve this, we propose to use detected visual objects to create these two tables for frame-level video filtering. Our study begins with investigating the existing object detection techniques. After that, we find object detection lacks the identification and connection abilities to accomplish the creation process alone. To supply these abilities, we further investigate three candidates, namely, recognizing-based, matching-based and tracking-based methods, to work with the object detection. Through analyzing the mechanism and evaluating the accuracy, we find that they are imperfect for identifying or connecting the visual objects. Accordingly, we propose a novel hybrid method that combines the matching-based and tracking-based methods to overcome the limitations. Our experiments show that the proposed method achieves higher accuracy and efficiency than the candidate methods. The subsequent analysis shows that the proposed method can efficiently support the frame-level video filtering using visual objects.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150103008
dc.relation.ispartof	World Wide Web	en_US
dc.relation.isbasedon	10.1007/s11280-017-0505-6	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Exploiting detected visual objects for frame-level video filtering	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	21	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0804 Data Format	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
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

© 2017, Springer Science+Business Media, LLC. Videos are generated at an unprecedented speed on the web. To improve the efficiency of access, developing new ways to filter the videos becomes a popular research topic. One on-going direction is using visual objects to perform frame-level video filtering. Under this direction, existing works create the unique object table and the occurrence table to maintain the connections between videos and objects. However, the creation process is not scalable and dynamic because it heavily depends on human labeling. To improve this, we propose to use detected visual objects to create these two tables for frame-level video filtering. Our study begins with investigating the existing object detection techniques. After that, we find object detection lacks the identification and connection abilities to accomplish the creation process alone. To supply these abilities, we further investigate three candidates, namely, recognizing-based, matching-based and tracking-based methods, to work with the object detection. Through analyzing the mechanism and evaluating the accuracy, we find that they are imperfect for identifying or connecting the visual objects. Accordingly, we propose a novel hybrid method that combines the matching-based and tracking-based methods to overcome the limitations. Our experiments show that the proposed method achieves higher accuracy and efficiency than the candidate methods. The subsequent analysis shows that the proposed method can efficiently support the frame-level video filtering using visual objects.

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http://hdl.handle.net/10453/124717