A fast visual line segment tracker

Neubert, P; Protzel, P; Vidal-Calleja, T; Lacroix, S

A fast visual line segment tracker

Neubert, P Protzel, P Vidal-Calleja, T

Lacroix, S

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2008, pp. 353 - 360
Issue Date:: 2008-11-24

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Field	Value	Language
dc.contributor.author	Neubert, P	en_US
dc.contributor.author	Protzel, P	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Lacroix, S	en_US
dc.date.issued	2008-11-24	en_US
dc.identifier.citation	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2008, pp. 353 - 360	en_US
dc.identifier.isbn	1424415063	en_US
dc.identifier.isbn	9781424415069	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29968
dc.description.abstract	We present a fast line segment tracker which does not require any knowledge about the motion of the camera nor the structure of the observed scene. It runs on 320 × 240 pixel images at 30 Hz. We adapted the RAPID tracker with a new way of handling multiple line hypotheses to deal with the simple model of a single line segment. We discuss the difficulty of using a χ2-test as merging criterion and also present a new approach to overcome it. Furthermore, instead of making assumptions about the camera motion, a constant velocity motion model to predict the line segment position in the following frame is used. We explain how to deal with the instability of the endpoint extraction in this motion model to avoid unintentional motion along the line. Finally, we present results on real world indoor and urban outdoor image sequences. © 2008 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA	en_US
dc.relation.isbasedon	10.1109/ETFA.2008.4638418	en_US
dc.title	A fast visual line segment tracker	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hamburg, Germany	en_US
dc.location.activity	Hamburg, GERMANY
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

We present a fast line segment tracker which does not require any knowledge about the motion of the camera nor the structure of the observed scene. It runs on 320 × 240 pixel images at 30 Hz. We adapted the RAPID tracker with a new way of handling multiple line hypotheses to deal with the simple model of a single line segment. We discuss the difficulty of using a χ2-test as merging criterion and also present a new approach to overcome it. Furthermore, instead of making assumptions about the camera motion, a constant velocity motion model to predict the line segment position in the following frame is used. We explain how to deal with the instability of the endpoint extraction in this motion model to avoid unintentional motion along the line. Finally, we present results on real world indoor and urban outdoor image sequences. © 2008 IEEE.

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http://hdl.handle.net/10453/29968