This paper describes models and algorithms for the real-time segmentation of foreground from
background layers in stereo video sequences. Automatic separation of layers
from color/contrast or from stereo alone is known to be error-prone. Here, color,
contrast and stereo matching information are fused to infer
layers accurately and efficiently. The first algorithm,
Layered Dynamic Programming (LDP), solves stereo in an
extended 6-state space that represents both foreground/background layers and
occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive
color model that is learned on the fly, and stereo disparities are obtained by dynamic programming.
The second algorithm, Layered Graph Cut (LGC), does
not directly solve stereo. Instead the stereo match likelihood is marginalized over
disparities to evaluate foreground and background hypotheses, and then fused with
a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut.
Both algorithms are evaluated with respect to ground truth data and found to have similar perfomance, substantially better than stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.