Real-time lane marker detection using template matching with RGB-D camera

Quach, CH; Tran, VL; Nguyen, DH; Nguyen, VT; Pham, MT; Phung, MD

Real-time lane marker detection using template matching with RGB-D camera

Quach, CH Tran, VL Nguyen, DH Nguyen, VT Pham, MT Phung, MD

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM 2018, 2018, 2018-January pp. 152 - 157
Issue Date:: 2018-03-26

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Field	Value	Language
dc.contributor.author	Quach, CH	en_US
dc.contributor.author	Tran, VL	en_US
dc.contributor.author	Nguyen, DH	en_US
dc.contributor.author	Nguyen, VT	en_US
dc.contributor.author	Pham, MT	en_US
dc.contributor.author	Phung, MD https://orcid.org/0000-0002-8870-5114	en_US
dc.date.issued	2018-03-26	en_US
dc.identifier.citation	Proceedings - 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM 2018, 2018, 2018-January pp. 152 - 157	en_US
dc.identifier.isbn	9781538629765	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126069
dc.description.abstract	© 2018 IEEE. This paper addresses the problem of lane detection which is fundamental for self-driving vehicles. Our approach exploits both colour and depth information recorded by a single RGB-D camera to better deal with negative factors such as lighting conditions and lane-like objects. In the approach, colour and depth images are first converted to a half-binary format and a 2D matrix of 3D points. They are then used as the inputs of template matching and geometric feature extraction processes to form a response map so that its values represent the probability of pixels being lane markers. To further improve the results, the template and lane surfaces are finally refined by principal component analysis and lane model fitting techniques. A number of experiments have been conducted on both synthetic and real datasets. The result shows that the proposed approach can effectively eliminate unwanted noise to accurately detect lane markers in various scenarios. Moreover, the processing speed of 20 frames per second under hardware configuration of a popular laptop computer allows the proposed algorithm to be implemented for real-time autonomous driving applications.	en_US
dc.relation.ispartof	Proceedings - 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SIGTELCOM 2018	en_US
dc.relation.isbasedon	10.1109/SIGTELCOM.2018.8325781	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Real-time lane marker detection using template matching with RGB-D camera	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0805 Distributed Computing	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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2018 IEEE. This paper addresses the problem of lane detection which is fundamental for self-driving vehicles. Our approach exploits both colour and depth information recorded by a single RGB-D camera to better deal with negative factors such as lighting conditions and lane-like objects. In the approach, colour and depth images are first converted to a half-binary format and a 2D matrix of 3D points. They are then used as the inputs of template matching and geometric feature extraction processes to form a response map so that its values represent the probability of pixels being lane markers. To further improve the results, the template and lane surfaces are finally refined by principal component analysis and lane model fitting techniques. A number of experiments have been conducted on both synthetic and real datasets. The result shows that the proposed approach can effectively eliminate unwanted noise to accurately detect lane markers in various scenarios. Moreover, the processing speed of 20 frames per second under hardware configuration of a popular laptop computer allows the proposed algorithm to be implemented for real-time autonomous driving applications.

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http://hdl.handle.net/10453/126069