Robust Segmentation of Hidden Layers in Video Sequences

In this paper, we propose a novel and robust method for extracting motion layers in video sequences. Taking advantage of temporal continuity, our framework considers both the visible and the hidden parts of each layer in order to increase robustness. Moreover, the hidden parts of the layers are recovered, which could be of great help in many high level vision tasks. Modeling the problem as a labeling task, we state it in a MRF-optimization framework and solve it with a graph-cut algorithm. Both synthetic and real video sequences show a visible layers extraction comparable to the one usually performed by state of the art methods, as well as a novel and successful segmentation of hidden layers