Previous video matting approaches mostly adopt the “binary segmentation + matting” strategy, i.e., first segment each frame into foreground and background regions, then extract the fine details of the foreground boundary using matting techniques. This framework has several limitations due to the fact that binary segmentation is employed. In this paper, we propose a new supervised video matting approach. Instead of applying binary segmentation, we explicitly model segmentation uncertainty in a novel tri-level segmentation procedure. The segmentation is done progressively, enabling us to handle difficult cases such as large topology changes, which are challenging to previous approaches. The tri-level segmentation results can be naturally fed into matting techniques to generate the final alpha mattes. Experimental results show that our system can generate high quality results with less user inputs than the state-of-theart methods.