Target　tracking　in　low-altitude　Unmanned　Aerial　Vehicle　(UAV)　videos　faces　many　technical　challenges　due　to　the　relatively　small　sizes,　various　orientation　changes　of　the　objects　and　diverse　scenes.　As　a　result,　the　tracking　performance　is　still　not　satisfactory.　In　this　paper,　we　propose　a　real-time　single-target　tracking　method　with　multiple　Region　Proposal　Networks　(RPNs)　and　Distance-Intersection-over-Union　(Distance-IoU)　Discriminative　Network　(DIDNet),　namely　MultiRPN-DIDNet,　in　which　ResNet50　is　used　as　the　backbone　network　for　feature　extraction.　Firstly,　an　instance-based　RPN　suitable　for　the　target　tracking　task　is　constructed　under　the　framework　of　Simases　Neural　Network.　RPN　is　to　perform　bounding　box　regression　and　classification,　in　which　channel　attention　mechanism　is　integrated　to　improve　the　representative　capability　of　the　deep　features.　The　RPNs　built　on　the　Block　2,　Block　3　and　Block　4　of　ResNet50　output　their　own　Regression　(Reg)　coefficients　and　Classification　scores　(Cls)　respectively,　which　are　weighted　and　then　fused　to　determine　the　high-quality　region　proposals.　Secondly,　a　DIDNet　is　designed　to　correct　the　candidate　target＇s　bounding　box　finely　through　the　fusion　of　multi-layer　features,　which　is　trained　with　the　Distance-IoU　loss.　Experimental　results　on　the　public　datasets　of　UAV20L　and　DTB70　show　that,　compared　with　the　state-of-the-art　UAV　trackers,　the　proposed　MultiRPN-DIDNet　can　obtain　better　tracking　performance　with　fewer　region　proposals　and　correction　iterations.　As　a　result,　the　tracking　speed　has　reached　33.9　frames　per　second　(FPS),　which　can　meet　the　requirements　of　real-time　tracking　tasks.