Self-calibration of traffic surveillance camera using motion tracking

Thi, TH; Lu, S; Zhang, J

Self-calibration of traffic surveillance camera using motion tracking

Thi, TH Lu, S Zhang, J

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Publication Type:: Conference Proceeding
Citation:: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2008, pp. 304 - 309
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Thi, TH	en_US
dc.contributor.author	Lu, S	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2008, pp. 304 - 309	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29723
dc.description.abstract	A statistical and computer vision approach using tracked moving vehicle shapes for auto-calibrating traffic surveillance cameras is presented. Vanishing point of the traffic direction is picked up from Linear Regression of all tracked vehicle points. Preliminary straightening model is then built to help collect statistics of the typical vehicle class traveling in each particular scene. Analysis on this class eventually helps to compute the complete calibration parameters. Results obtained from the validation step against traditional methods in different traffic locations demonstrate its desirable accuracy with much more flexibility and reliability. © 2008 IEEE.	en_US
dc.relation.ispartof	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC	en_US
dc.relation.isbasedon	10.1109/ITSC.2008.4732673	en_US
dc.title	Self-calibration of traffic surveillance camera using motion tracking	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, China	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

A statistical and computer vision approach using tracked moving vehicle shapes for auto-calibrating traffic surveillance cameras is presented. Vanishing point of the traffic direction is picked up from Linear Regression of all tracked vehicle points. Preliminary straightening model is then built to help collect statistics of the typical vehicle class traveling in each particular scene. Analysis on this class eventually helps to compute the complete calibration parameters. Results obtained from the validation step against traditional methods in different traffic locations demonstrate its desirable accuracy with much more flexibility and reliability. © 2008 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/29723