Object segmentation in video is an important subject in computer vision and has gained various research and application values. The online automatic object segmentation method is proposed in this paper; this method fuses appearance and motion features. First

the object points were roughly estimated by employing appearance and motion boundaries. Then

we utilized these estimated object points to refine the appearance model (GMM) of the previous frame as current appearance model. Second

a Markov random field (MRF) model was constructed by taking the superpixels as nodes

and integrating the appearance model and the location prior. Therefore

the object segmentation can be converted to an energy minimization problem

which is optimized by graph cut in this paper. After extensive experiments which included comparison analysis of five approaches and component analysis of the proposed approach on two datasets

the proposed approach improved accuracy of segmentation by at least 44.8% than other approaches

and achieved higher efficiency of segmentation. The proposed algorithm achieved online automatic object segmentation by fusing appearance and motion features

and obtained good segmentation performance. Furthermore

this algorithm was also robust in several complicated scenes.