Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

Dongkwon Jin; Wonhui Park; Seong Gyun Jeong; Heeyeon Kwon; Chang Su Kim

doi:10.1109/CVPR52688.2022.01665

Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

Dongkwon Jin
, Wonhui Park
, Seong Gyun Jeong
, Heeyeon Kwon
, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

48 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Publisher	IEEE Computer Society
Pages	17142-17150
Number of pages	9
ISBN (Electronic)	9781665469463
DOIs	https://doi.org/10.1109/CVPR52688.2022.01665
Publication status	Published - 2022
Event	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States Duration: 2022 Jun 19 → 2022 Jun 24

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2022-June
ISSN (Print)	1063-6919

Conference

Conference	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/Territory	United States
City	New Orleans
Period	22/6/19 → 22/6/24

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Navigation and autonomous driving
Scene analysis and understanding
Vision applications and systems

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR52688.2022.01665

Cite this

Jin, D., Park, W., Jeong, S. G., Kwon, H., & Kim, C. S. (2022). Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 (pp. 17142-17150). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2022-June). IEEE Computer Society. https://doi.org/10.1109/CVPR52688.2022.01665

Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes. / Jin, Dongkwon; Park, Wonhui; Jeong, Seong Gyun et al.
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE Computer Society, 2022. p. 17142-17150 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2022-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jin, D, Park, W, Jeong, SG, Kwon, H & Kim, CS 2022, Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes. in Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, IEEE Computer Society, pp. 17142-17150, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, United States, 22/6/19. https://doi.org/10.1109/CVPR52688.2022.01665

Jin D, Park W, Jeong SG, Kwon H, Kim CS. Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE Computer Society. 2022. p. 17142-17150. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR52688.2022.01665

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title = "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes",

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.",

keywords = "Navigation and autonomous driving, Scene analysis and understanding, Vision applications and systems",

author = "Dongkwon Jin and Wonhui Park and Jeong, \{Seong Gyun\} and Heeyeon Kwon and Kim, \{Chang Su\}",

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year = "2022",

doi = "10.1109/CVPR52688.2022.01665",

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series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

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T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022

AU - Jin, Dongkwon

AU - Park, Wonhui

AU - Jeong, Seong Gyun

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AU - Kim, Chang Su

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AB - 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.

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Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this