Aiming　at　the　problems　of　kernelized　correlation　filter　tracking　algorithm　such　as　scale　change,　severe　occlusion　and　tracking　failure　under　similar　target　tracking,　a　long-term　target　tracking　algorithm　based　on　KCF　is　proposed.　The　algorithm　introduces　color　information　into　the　tracker,　and　the　scale　information　makes　the　algorithm　adapt　to　the　more　complex　situation　when　tracking.　It　is　worth　noting　that　the　trackers　train　two　models,　one　to　represent　coordinates　and　the　other　to　characterize　confidence.　In　addition,　training　online　support　vector　machine　classifiers　is　also　crucial.　This　article　uses　a　multi-expert　tracking　strategy　to　enable　longterm　tracking　to　be　re-detected　in　the　event　of　a　failed　tracking　condition.　The　validity　of　the　proposed　algorithm　is　verified　by　the　OTB-2013　[1]　evaluation　sequence.　Compared　with　several　other　classical　algorithms,　this　algorithm　has　been　significantly　improved.　In　the　target　occurrence　scale,　occlusion,　deformation　and　other　interference　cases　with　strong　robustness.　©　2018　IEEE.