In this paper, we present a two-stage unsupervised foreground object
segmentation network tailored for dynamic scenes affected by atmospheric
turbulence. In the first stage, we utilize averaged optical flow from
turbulence-distorted image sequences to feed a novel region-growing algorithm,
crafting preliminary masks for each moving object in the video. In the second
stage, we employ a U-Net architecture with consistency and grouping losses to
further refine these masks optimizing their spatio-temporal alignment. Our
approach does not require labeled training data and works across varied
turbulence strengths for long-range video. Furthermore, we release the first
moving object segmentation dataset of turbulence-affected videos, complete with
manually annotated ground truth masks. Our method, evaluated on this new
dataset, demonstrates superior segmentation accuracy and robustness as compared
to current state-of-the-art unsupervised methods.

本文提出了一个针对受大气湍流影响动态场景的两阶段无监督前景目标分割网络。在第一阶段，我们利用湍流变形的图像序列的平均光流来提供一个新颖的区域生长算法，为视频中的每个移动物体构建初步的掩膜。在第二阶段，我们采用具有一致性和分组损失的 U-Net 架构进一步优化这些掩膜，优化它们的时空对齐。我们的方法不需要标记的训练数据，并且适用于长距离视频的各种湍流强度。此外，我们发布了第一个受湍流影响的移动目标分割数据集，包含手动注释的地面实况掩膜。在这个新数据集上评估，我们的方法相比当前最先进的无监督方法展现出了优越的分割准确性和鲁棒性。