Fast and robust head detection with arbitrary pose and occlusion

Zhang, T; Yang, Z; Jia, W; Wu, Q; Yang, J; He, X

Fast and robust head detection with arbitrary pose and occlusion

Zhang, T Yang, Z Jia, W

Wu, Q

Yang, J He, X

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2015, 74 (21), pp. 9365 - 9385
Issue Date:: 2015-11-29

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Field	Value	Language
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2015-11-29	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2015, 74 (21), pp. 9365 - 9385	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34971
dc.description.abstract	© 2014, Springer Science+Business Media New York. Head detection in images and videos plays an important role in a wide range of computer vision and surveillance applications. Aiming to detect heads with arbitrarily occluded faces and head pose, in this paper, we propose a novel Gaussian energy function based algorithm for elliptical head contour detection. Starting with the localization of head and shoulder by an improved Gaussian Mixture Model (GMM) approach, the precise head contour is obtained by making use of the Omega shape formed from the head and shoulder. Experimental results on several benchmark datasets demonstrate the superiority of the proposed idea over the state-of-the-art in both detection accuracy and processing speed, even though there are various types of severe occlusions in faces.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-014-2110-3	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Fast and robust head detection with arbitrary pose and occlusion	en_US
dc.type	Journal Article
utslib.citation.volume	21	en_US
utslib.citation.volume	74	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 - CRIN - Realtime Information Networks
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.issue	21	en_US
pubs.publication-status	Published	en_US
pubs.volume	74	en_US

Abstract:

© 2014, Springer Science+Business Media New York. Head detection in images and videos plays an important role in a wide range of computer vision and surveillance applications. Aiming to detect heads with arbitrarily occluded faces and head pose, in this paper, we propose a novel Gaussian energy function based algorithm for elliptical head contour detection. Starting with the localization of head and shoulder by an improved Gaussian Mixture Model (GMM) approach, the precise head contour is obtained by making use of the Omega shape formed from the head and shoulder. Experimental results on several benchmark datasets demonstrate the superiority of the proposed idea over the state-of-the-art in both detection accuracy and processing speed, even though there are various types of severe occlusions in faces.

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http://hdl.handle.net/10453/34971