OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
This work addresses lane detection in videos for autonomous driving applications, presenting an incremental improvement over prior methods.
The paper tackles video lane detection by proposing an occlusion-aware memory-based refinement (OMR) module that processes features and obstacle masks recursively, along with a novel data augmentation scheme, and reports outperforming existing techniques on video lane datasets.
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.