Key points trajectory and multi-level depth distinction based refinement for video mirror and glass segmentation

Abstract

AbstractMirror and glass are ubiquitous materials in the 3D indoor living environment. However, the existing vision system always tends to neglect or misdiagnose them since they always perform the special visual feature of reflectivity or transparency, which causes severe consequences, i.e., a robot or drone may crash into a glass wall or be wrongly positioned by the reflections in mirrors, or wireless signals with high frequency may be influenced by these high-reflective materials. The exploration of segmenting mirrors and glass in static images has garnered notable research interest in recent years. However, accurately segmenting mirrors and glass within dynamic scenes remains a formidable challenge, primarily due to the lack of a high-quality dataset and effective methodologies. To accurately segment the mirror and glass regions in videos, this paper proposes key points trajectory and multi-level depth distinction to improve the segmentation quality of mirror and glass regions that are generated by any existing segmentation model. Firstly, key points trajectory is used to extract the special motion feature of reflection in the mirror and glass region. And the distinction in trajectory is used to remove wrong segmentation. Secondly, a multi-level depth map is generated for region and edge segmentation which contributes to the accuracy improvement. Further, an original dataset for video mirror and glass segmentation (MAGD) is constructed, which contains 9,960 images from 36 videos with corresponding manually annotated masks. Extensive experiments demonstrate that the proposed method consistently reduces the segmentation errors generated from various state-of-the-art models and reach the highest successful rate at 0.969, mIoU (mean Intersection over Union) at 0.852, and mPA (mean Pixel Accuracy) at 0.950, which is around 40% - 50% higher on average on an original video mirror and glass dataset.