  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.
