Video Instance Lane Detection via Deep Temporal and Geometry Consistency Constraints

Video instance lane detection is one of the most important tasks in autonomous driving.Due to the very sparse region and weak context in lane annotations, accurately detecting instance-level lanes in real-world traffic scenarios is challenging, especially for scenes with occlusion, bad weather conditions, dim or dazzling lights.Current methods mainly address this problem by integrating features of adjacent video frames to simply encourage temporal constancy for image-level lane detectors. However, most of them ignore lane shape constraint of adjacent frames and geometry consistency of individual lanes, thereby harming the performance of video instance lane detection. In this paper, we propose TGC-Net via temporal and geometry consistency constraints for reliable video instance lane detection. Specifically, we devise a temporal recurrent feature-shift aggregation module (T-RESA) to learn spatio-temporal lane features along horizontal, vertical, and temporal directions of the feature tensor. We further impose temporal consistency constraint by encouraging spatial distribution consistency among the lane features of adjacent frames. Besides, we devise two effective geometry constraints to ensure the integrity and continuity of lane predictions by leveraging pairwise point affinity loss and vanishing point guided geometric context, respectively. Extensive experiments on public benchmark dataset show that our TGC-Net quantitatively and qualitatively outperforms state-of-the-art video instance lane detectors and video object segmentation competitors. Our code and our results have been released at https://github.com/wmq12345/TGC-Net.