Visual Attention Based Motion Object Detection and Trajectory Tracking

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

Visual Attention Based Motion Object Detection and Trajectory Tracking

Xu, M

Guo, W Xu, C Ma, S

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: 11th Pacific Rim Conference on Multimedia, 2010, pp. 462 - 470
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Guo, W	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Ma, S	en_US
dc.contributor.editor	Qiu, G	en_US
dc.contributor.editor	Lam, KM	en_US
dc.contributor.editor	Kiya, H	en_US
dc.contributor.editor	Xue, XY	en_US
dc.contributor.editor	Kuo, CCJ	en_US
dc.contributor.editor	Lew, MS	en_US
dc.date	2010-09-21	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	11th Pacific Rim Conference on Multimedia, 2010, pp. 462 - 470	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31458
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.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	11th Pacific Rim Conference on Multimedia	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	Conference Proceeding
utslib.location	Shanghai, China	en_US
utslib.location.activity	Shanghai China	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai China
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.consider-herdc	false	en_US
pubs.place-of-publication	Shanghai, China	en_US
pubs.start-date	2010-09-21	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.

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http://hdl.handle.net/10453/31458