A new method using the coarsely sampled motion vector fields to detect moving object regions from dynamic background is proposed.The proposed method estimates global motion parameters b y analyzing properties of motion vectors,and compensates the global motion for removing background motion in mo tion vector fields.The motion vector compensated errors generated from global motion compensation are then utilized t o create max-tree data structure representation for video frames.The connected regions that have almost the same movement in video frames are hierarchically represented,and the initial classifications of motion areas are completed.According to the spatial connectivity and motion consistency of the same motion object,the regional simi larity measure criteria are selected and utilized for regions splitting and merging.The connected areas with similar move ment are merged,and the moving object regions are then detected.The segmented moving object regions are then treated as outliers and rejected in the next round of global motion estimation.The alternation between global motion estimation an d motion segmentation not only speeds up the computation,but also improves the precision of calculation and detection.E xperimental results demonstrate that the proposed approach can effectively compensate the background motion and detect mo ving object regions from dynamic background.