Video　object　segmentation　(VOS)　is　a　research　hotspot　in　the　field　of　computer　vision.Traditional　VOS　based　on　deep　learning　fine-tunes　the　deep　network　online,　which　leads　to　long　time-consuming　segmentation　and　is　difficult　to　meet　real-time　requirements.Therefore,　we　propose　a　fast　VOS　method.First,　the　weight-shared　siamese　encoder　subnet　maps　the　reference　stream　and　the　target　stream　to　the　same　feature　space;　so　that　the　same　objects　have　similar　features.Then,　the　global　feature　extraction　subnet　matches　the　features　similar　to　the　given　object　to　locate　the　object.Finally,　the　decoder　subnet　restores　the　object　features　and　gets　edge　information　by　connecting　the　low-level　features　of　target　stream　to　output　the　mask.Experiments　on　public　benchmark　datasets　show　that　our　method　improves　the　speed　significantly　and　achieves　good　performance.　©　2020,　Chinese　Institute　of　Electronics.　All　right　reserved.