Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
Abstract
A novel algorithm to detect road lanes in the eigenlane 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 candidates 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 network, 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.
Cite
Text
Jin et al. "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01665Markdown
[Jin et al. "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/jin2022cvpr-eigenlanes/) doi:10.1109/CVPR52688.2022.01665BibTeX
@inproceedings{jin2022cvpr-eigenlanes,
title = {{Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes}},
author = {Jin, Dongkwon and Park, Wonhui and Jeong, Seong-Gyun and Kwon, Heeyeon and Kim, Chang-Su},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2022},
pages = {17163-17171},
doi = {10.1109/CVPR52688.2022.01665},
url = {https://mlanthology.org/cvpr/2022/jin2022cvpr-eigenlanes/}
}