Visual attention based motion object detection and trajectory tracking

Guo, W; Xu, C; Ma, S; Xu, M

Visual attention based motion object detection and trajectory tracking

Guo, W Xu, C Ma, S Xu, M

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Publication Type:: Chapter
Citation:: 2010, 6298 LNCS pp. 462 - 470
Issue Date:: 2010-11-09

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Field	Value	Language
dc.contributor.author	Guo, W	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Ma, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2010-11-09	en_US
dc.identifier.citation	2010, 6298 LNCS pp. 462 - 470	en_US
dc.identifier.isbn	3642156959	en_US
dc.identifier.isbn	9783642156953	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12967
dc.description.abstract	A motion trajectory tracking method using a novel visual attention model and kernel density estimation is proposed in this paper. As a crucial step, moving objects detection is based on visual attention. The visual attention model is built by combination of the static and motion feature attention map and a Karhunen-Loeve transform (KLT) distribution map. Since the visual attention analysis is conducted on object level instead of pixel level, the proposed method can detect any kinds of motion objects provided saliency without the affection of objects appearance and surrounding circumstance. After locating the region of moving object, the kernel density is estimated for trajectory tracking. The experimental results show that the proposed method is promising for moving objects detection and trajectory tracking. © 2010 Springer-Verlag.	en_US
dc.relation.isbasedon	10.1007/978-3-642-15696-0_43	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Visual attention based motion object detection and trajectory tracking	en_US
dc.type	Chapter
utslib.citation.volume	6298 LNCS	en_US
utslib.for	080305 Multimedia Programming	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
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6298 LNCS	en_US

Abstract:

A motion trajectory tracking method using a novel visual attention model and kernel density estimation is proposed in this paper. As a crucial step, moving objects detection is based on visual attention. The visual attention model is built by combination of the static and motion feature attention map and a Karhunen-Loeve transform (KLT) distribution map. Since the visual attention analysis is conducted on object level instead of pixel level, the proposed method can detect any kinds of motion objects provided saliency without the affection of objects appearance and surrounding circumstance. After locating the region of moving object, the kernel density is estimated for trajectory tracking. The experimental results show that the proposed method is promising for moving objects detection and trajectory tracking. © 2010 Springer-Verlag.

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http://hdl.handle.net/10453/12967