This paper presents a video object motion segmentation method for object tracking in visual surveillance. In the first step, the frames are first decomposed into small facets (regions), using colour information. Then, based on the detected motion, the motion segmentation is performed at facet level. A Bayesian approach is applied in clustering facets into moving objects and tracking moving video objects. Experiments have verified that the proposed method can efficiently tackle the complexity of video motion tracking.