Existing　deployed　Unmanned　Aerial　Vehicles　(UAVs)　visual　trackers　are　usually　based　on　the　correlation　filter　framework.　Although　thesemethods　have　certain　advantages　of　lowcomputational　complexity,　the　tracking　performance　of　small　targets　and　fast　motion　scenarios　is　not　satisfactory.　In　this　paper,　we　present　a　novel　multilevel　prediction　Siamese　network　(MLPS)　for　object　tracking　in　UAV　videos,　which　consists　of　Siamese　feature　extraction　module　and　multi-level　prediction　module.　The　multi-level　prediction　module　can　make　full　use　of　the　characteristics　of　each　layer　features　to　achieve　robust　evaluation　of　targets　with　different　scales.　Meanwhile,　for　small-size　target　tracking,　we　design　a　residual　feature　fusion　block,　which　is　used　to　constrain　the　low-level　feature　representation　by　using　high-level　abstract　semantics,　and　obtain　the　improvement　of　the　tracker＇s　ability　to　distinguish　scene　details.　In　addition,　we　propose　a　layer　attention　fusion　block　which　is　sensitive　to　the　informative　features　of　each　layers　to　achieve　adaptive　fusion　of　different　levels　of　correlation　responses　by　dynamically　balancing　the　multi-layer　features.　Sufficient　experiments　on　several　UAV　tracking　benchmarks　demonstrate　that　MLPS　achieves　state-of-the-art　performance　and　runs　at　a　speed　over　97　FPS.　(c)　2020　Elsevier　B.V.　All　rights　reserved.