Efficient people counting with limited manual interferences

Xu, J; Wu, Q; Zhang, J; Silk, B; Ngo, GT; Tang, Z

Efficient people counting with limited manual interferences

Xu, J Wu, Q

Zhang, J

Silk, B Ngo, GT Tang, Z

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Publication Type:: Conference Proceeding
Citation:: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 2015
Issue Date:: 2015-01-12

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Field	Value	Language
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Silk, B	en_US
dc.contributor.author	Ngo, GT	en_US
dc.contributor.author	Tang, Z	en_US
dc.date.issued	2015-01-12	en_US
dc.identifier.citation	2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 2015	en_US
dc.identifier.isbn	9781479954094	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37749
dc.description.abstract	© 2014 IEEE. People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: a) counting based on individual human detection; b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges, it has been considered as a robust method particularly on a complicated environments. An efficient regression based method is proposed in this paper, which can be well adopted into any existing video surveillance system. It adopts color based segmentation to extract foreground regions in images. Regression is established based on the foreground density and the number of people. This method is fast and can deal with lighting condition changes. Experiments on public datasets and one captured dataset have shown the effectiveness and robustness of the method.	en_US
dc.relation.ispartof	2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014	en_US
dc.relation.isbasedon	10.1109/DICTA.2014.7008106	en_US
dc.title	Efficient people counting with limited manual interferences	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: a) counting based on individual human detection; b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges, it has been considered as a robust method particularly on a complicated environments. An efficient regression based method is proposed in this paper, which can be well adopted into any existing video surveillance system. It adopts color based segmentation to extract foreground regions in images. Regression is established based on the foreground density and the number of people. This method is fast and can deal with lighting condition changes. Experiments on public datasets and one captured dataset have shown the effectiveness and robustness of the method.

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http://hdl.handle.net/10453/37749