Moving-object segmentation is the key issue of Telepresence systems. With monocular camera--based segmentation methods, desirable segmentation results are hard to obtain in challenging scenes with ambiguous color, illumination changes, and shadows. Approaches based on depth sensors often cause holes inside the object and missegmentations on the object boundary due to inaccurate and unstable estimation of depth data. This work proposes an adaptive multi-cue decision fusion method based on Kinect (which integrates a depth sensor with an RGB camera). First, the algorithm obtains an initial foreground mask based on the depth cue. Second, the algorithm introduces a postprocessing framework to refine the segmentation results, which consists of two main steps: (1) automatically adjusting the weight of two weak decisions to identify foreground holes based on the color and contrast cue separately; and (2) refining the object boundary by integrating the motion probability weighted temporal prior, color likelihood, and smoothness constraint. The extensive experiments we conducted demonstrate that our method can segment moving objects accurately and robustly in various situations in real time.