  Deep convolutional neural networks (CNNs) based approaches have achieved
great performance in video matting. Many of these methods can produce accurate
alpha estimation for the target body but typically yield fuzzy or incorrect
target edges. This is usually caused by the following reasons: 1) The current
methods always treat the target body and edge indiscriminately; 2) Target body
dominates the whole target with only a tiny proportion target edge. For the
first problem, we propose a CNN-based module that separately optimizes the
matting target body and edge (SOBE). And on this basis, we introduce a
real-time, trimap-free video matting method via progressively optimizing the
matting target body and edge (POBEVM) that is much lighter than previous
approaches and achieves significant improvements in the predicted target edge.
For the second problem, we propose an Edge-L1-Loss (ELL) function that enforces
our network on the matting target edge. Experiments demonstrate our method
outperforms prior trimap-free matting methods on both Distinctions-646 (D646)
and VideoMatte240K(VM) dataset, especially in edge optimization.
