How to automatically discover salient objects in video and further perform accurate object segmentation is a challenging problem in computer vision. Here, fast salient object segmentation method based on edge-preserving filtering is proposed. Firstly, the salient object discovery is formulated as an energy minimization problem, which fuses the appearance and motion features. Then, a Markov random field (MRF) model, integrating the Gaussian mixture model (GMM) of appearance, the location prior, and the spatial-temporal smoothness, is constructed for accurate segmentation, and is efficiently optimized by graph cut. Moreover, an edge-preserving-based method is presented to improve the segmentation efficiency with a little loss of accuracy. Finally, extensive experiments on two datasets suggest that the proposed method performance is better than that of other five methods, and the accelerated version can speed up to 2 times of the original one.