OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
<|reference_start|>OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection: 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.<|reference_end|>
@article{jin2024omr:,
    title={OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection},
    author={Dongkwon Jin and Chang-Su Kim},
    journal={arXiv preprint arXiv:2408.07486},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07486},
    primaryClass={cs.CV}
}