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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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 17142 - 17150
Hauptverfasser: Jin, Dongkwon, Park, Wonhui, Jeong, Seong-Gyun, Kwon, Heeyeon, Kim, Chang-Su
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2022
Schlagworte:
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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Zusammenfassung: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.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.01665