Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.

从场景中提取准确的前景对象是许多视频应用程序中必不可少的步骤。传统的背景减法算法可以生成粗略的估计，但是生成高质量的蒙版需要具有大量人为干预的专业软件，例如，提供trimaps或标记关键帧。在存在静态但不完美背景的应用中，我们提出了一种自动前景提取方法。例如拍摄和监视，可以在物体进入场景之前或离开场景之后捕获背景。与最先进的背景减法，视频分割和Alpha遮罩方法相比，我们提出的方法非常健壮，并且可以产生更好的估计。我们方法的关键创新是一种新颖的信息融合技术。融合框架使我们能够整合Alpha遮罩，背景减法和图像降噪的各个优势，以产生总体更好的估计。当处理背景不完美的复杂场景时，这种集成特别重要。我们展示了如何开发框架以及如何构建各个组件。进行了广泛的实验和消融研究，以评估该方法。