Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

A novel algorithm to detect road lanes in the eigen-lane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M app...

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Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 17142 - 17150
Main Authors: Jin, Dongkwon, Park, Wonhui, Jeong, Seong-Gyun, Kwon, Heeyeon, Kim, Chang-Su
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2022
Subjects:
Codes
Computer vision
Image analysis
Machine vision
Navigation
Navigation and autonomous driving; Scene analysis and understanding; Vision applications and systems
Roads
Training
ISSN:1063-6919
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Abstract A novel algorithm to detect road lanes in the eigen-lane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candi-dates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection net-work, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes.
AbstractList A novel algorithm to detect road lanes in the eigen-lane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candi-dates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection net-work, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes.
Author Jeong, Seong-Gyun
Kim, Chang-Su
Kwon, Heeyeon
Park, Wonhui
Jin, Dongkwon
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Snippet A novel algorithm to detect road lanes in the eigen-lane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven...
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SubjectTerms Codes
Computer vision
Image analysis
Machine vision
Navigation
Navigation and autonomous driving; Scene analysis and understanding; Vision applications and systems
Roads
Training
Title Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
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