  Due to the difficulty of solving the matting problem, lots of methods use
some kinds of assistance to acquire high quality alpha matte. Green screen
matting methods rely on physical equipment. Trimap-based methods take manual
interactions as external input. Background-based methods require a
pre-captured, static background. The methods are not flexible and convenient
enough to use widely. Trimap-free methods are flexible but not stable in
complicated video applications. To be stable and flexible in real applications,
we propose an adaptive background matting method. The user first captures their
videos freely, moving the cameras. Then the user captures the background video
afterwards, roughly covering the previous captured regions. We use dynamic
background video instead of static background for accurate matting. The
proposed method is convenient to use in any scenes as the static camera and
background is no more the limitation. To achieve this goal, we use background
matching network to find the best-matched background frame by frame from
dynamic backgrounds. Then, robust semantic estimation network is used to
estimate the coarse alpha matte. Finally, we crop and zoom the target region
according to the coarse alpha matte, and estimate the final accurate alpha
matte. In experiments, the proposed method is able to perform comparably
against the state-of-the-art matting methods.
