Visual　object　tracking　in　unmanned　aerial　vehicle　(UAV)　videos　plays　an　important　role　in　a　variety　of　fields,　such　as　traffic　data　collection,　traffic　monitoring,　as　well　as　film　and　television　shooting.　However,　it　is　still　challenging　to　track　the　target　robustly　in　UAV　vision　task　due　to　several　factors　such　as　appearance　variation,　background　clutter,　and　severe　occlusion.　In　this　paper,　we　propose　a　novel　two-stage　UAV　tracking　framework,　which　includes　a　target　detection　stage　based　on　multifeature　discrimination　and　a　bounding-box　estimation　stage　based　on　the　instance-aware　attention　network.　In　the　target　detection　stage,　we　explore　a　feature　representation　scheme　for　a　small　target　that　integrates　handcrafted　features,　low-level　deep　features,　and　high-level　deep　features.　Then,　the　correlation　filter　is　used　to　roughly　predict　target　location.　In　the　bounding-box　estimation　stage,　an　instance-aware　intersection　over　union　(IoU)-Net　is　integrated　together　with　an　instance-aware　attention　network　to　estimate　the　target　size　based　on　the　bounding-box　proposals　generated　in　the　target　detection　stage.　Extensive　experimental　results　on　the　UAV123　and　UAVDT　datasets　show　that　our　tracker,　running　at　over　25　frames　per　second　(FPS),　has　superior　performance　as　compared　with　state-of-the-art　UAV　visual　tracking　approaches.