UAVs　have　been　widely　used　in　military　and　civilian　applications,　and　target　tracking　technology　is　one　of　the　key　technologies　for　UAV　applications.　Aimed　at　the　problem　that　the　target　is　prone　to　scale　change　and　occlusion　during　the　target　tracking　process　of　the　UAV,　an　adaptive　UAV　video　target　tracking　algorithm　based　on　residual　learning　is　proposed.　Firstly,　by　combining　the　advantages　of　residual　learning　and　dilated　convolution,　a　depth　network　is　constructed　to　extract　target　features　and　overcome　the　problem　of　network　degradation.　Secondly,　the　extracted　feature　information　is　input　into　the　kernel　correlation　filtering　algorithm,　and　a　positioning　filter　is　constructed　to　determine　the　central　position　of　the　target.　Finally,　adaptive　segmentation　is　performed　according　to　the　different　appearance　characteristics　of　the　target　and　the　scaling　coefficient　of　the　target　scale　is　calculated.　The　simulation　results　show　that　the　algorithm　can　effectively　deal　with　the　influence　of　scale　change　and　occlusion　on　tracking　performance,　and　has　higher　tracking　success　rate　and　accuracy　than　other　comparison　algorithms.　©　2020,　Editorial　Board　of　JBUAA.　All　right　reserved.