Head detection for video surveillance based on categorical hair and skin colour models

Zhang, Z; Gunes, H; Piccardi, M

Head detection for video surveillance based on categorical hair and skin colour models

Zhang, Z Gunes, H Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2009, pp. 1137 - 1140
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Gunes, H	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2009, pp. 1137 - 1140	en_US
dc.identifier.isbn	9781424456543	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10736
dc.description.abstract	We propose a new robust head detection algorithm that is capable of handling significantly different conditions in terms of viewpoint, tilt angle, scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. We construct a categorical model for hair and skin, separately, and train the models for four categories of hair (brown, red, blond and black) and three categories of skin representing the different illumination conditions (bright, standard and dark). The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on human anatomy. The experimental results validate the usability of the proposed algorithm in various video surveillance and multimedia applications. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2009.5413535	en_US
dc.title	Head detection for video surveillance based on categorical hair and skin colour models	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Cairo, Egypt	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	open_access
pubs.publication-status	Published	en_US

Abstract:

We propose a new robust head detection algorithm that is capable of handling significantly different conditions in terms of viewpoint, tilt angle, scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. We construct a categorical model for hair and skin, separately, and train the models for four categories of hair (brown, red, blond and black) and three categories of skin representing the different illumination conditions (bright, standard and dark). The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on human anatomy. The experimental results validate the usability of the proposed algorithm in various video surveillance and multimedia applications. ©2009 IEEE.

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http://hdl.handle.net/10453/10736