Video　object　segmentation　(VOS)　is　a　research　hotspot　in　the　field　of　computer　vision.　Traditional　video　object　segmentation　methods　based　on　deep　learning　have　some　problems　such　as　difficulty　in　adapting　to　the　change　of　object　appearance　and　low　segmentation　speed.　In　this　manuscript,　we　propose　a　robust　VOS　method　based　on　motion-aware　region　of　interest　(ROI)　prediction　and　adaptive　reference　updating.　Firstly,　based　on　the　historical　movement　trajectory　of　target　region　to　perceive　motion　trend　dynamically,　we　predict　the　motion-aware　ROI　of　target　object　in　the　current　frame　and　use　it　as　the　input　of　segmentation　network.　Then,　in　order　to　adapt　to　the　appearance　changes　of　target　in　the　video,　the　adaptive　updating　strategy　of　reference　is　given　to　dynamically　update　the　reference　frame　during　the　segmentation　process.　Finally,　VOS　Siamese　network　is　designed　for　fast　segmentation.　Experiments　on　three　public　benchmark　datasets,　DAVIS-2016　and　DAVIS-2017,　show　that　the　proposed　method　performs　better　than　the　state-of-the-art　approaches.