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.
Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon and Chang-Su Kim,
"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Laness," accepted to Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[paper] [supp] [code] [video]