Semi-superised　video　object　segmentation(SVOS)　is　a　research　hotspot　in　the　field　of　computer　vision.　Most　semi-supervised　video　object　segmentation　methods　lack　the　ability　to　discriminate　similar　object,　and　the　traditional　mask　propagation　method　is　weak　in　guiding　the　model.　This　paper　proposes　a　semi-supervised　video　object　segmentation　method　based　on　foreground　perception　visual　attention.　The　three-stream　Siamese　encoder　maps　the　input　frame　to　the　same　feature　space,　so　that　the　same　objects　have　similar　features.　Visual　attention　based　on　foreground　perception　calculates　the　similarity　of　encoder　features　and　highlights　the　foreground　through　the　mask,　so　as　to　focus　on　the　given　object　and　improve　the　model　discrimination.　The　decoder　based　on　residual　refinement　fuses　the　low-level　features　of　the　current　frame　to　gradually　improve　the　segmentation　details.　Experiments　on　public　benchmark　datasets　show　that　the　proposed　method　can　deal　with　the　similar　confusion　of　the　object　and　track　the　given　object　accurately.　©　2022,　Chinese　Institute　of　Electronics.　All　right　reserved.