OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
Abstract
A novel algorithm for video lane detection is proposed in this paper. First, we extract a feature map for a current frame and detect a latent mask for obstacles occluding lanes. Then, we enhance the feature map by developing an occlusion-aware memory-based refinement (OMR) module. It takes the obstacle mask and feature map from the current frame, previous output, and memory information as input, and processes them recursively in a video. Moreover, we apply a novel data augmentation scheme for training the OMR module effectively. Experimental results show that the proposed algorithm outperforms existing techniques on video lane datasets. Our codes are available at https://github.com/dongkwonjin/OMR.
Cite
Text
Jin and Kim. "OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73414-4_8Markdown
[Jin and Kim. "OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/jin2024eccv-omr/) doi:10.1007/978-3-031-73414-4_8BibTeX
@inproceedings{jin2024eccv-omr,
title = {{OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection}},
author = {Jin, Dongkwon and Kim, Chang-Su},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73414-4_8},
url = {https://mlanthology.org/eccv/2024/jin2024eccv-omr/}
}