TY - GEN
T1 - Eigenlanes
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
AU - Jin, Dongkwon
AU - Park, Wonhui
AU - Jeong, Seong Gyun
AU - Kwon, Heeyeon
AU - Kim, Chang Su
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. NRF-2021R1A4A1031864 and No. NRF-2022R1A2B5B03002310).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
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.
KW - Navigation and autonomous driving
KW - Scene analysis and understanding
KW - Vision applications and systems
UR - http://www.scopus.com/inward/record.url?scp=85128053476&partnerID=8YFLogxK
U2 - 10.1109/CVPR52688.2022.01665
DO - 10.1109/CVPR52688.2022.01665
M3 - Conference contribution
AN - SCOPUS:85128053476
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 17142
EP - 17150
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PB - IEEE Computer Society
Y2 - 19 June 2022 through 24 June 2022
ER -