In this paper two efficient unsupervised video object segmentation approaches are proposed and then extensively compared in terms of computational cost and quality of segmentation results. Both methods exploit depth information. In particular a depth segments map is initially estimated by analyzing a stereo-scopic pair of frames and applying a segmentation algorithm. In the first a "Constrained Fusion of Color Segments" (CFCS) in which video object segmentation is performed by fusion of color segments according to a depth similarity criterion. In the second approach firstly a dilated version of the boundary of each depth segment is produced and several feature points are estimated on this dilated boundary. Then for each initial point a normalized Motion Geometric Space (MGS) is created which determines the only allowed way the point can move onto. In the last step each initial point moves onto its MGS and stops according to a weighted stop-function. Experiments on real life stereoscopic sequences are presented to exhibit the speed and accuracy of the proposed schemes.