In　the　visual　tracking,　correlation　filtering　(CF)　based　on　tracking　algorithms　have　shown　favorable　performance　in　recent　years,　and　have　the　impressive　performance　on　benchmark　datasets.　However,　the　tracking　model　has　limited　information　about　their　context　and　can　easily　drift　in　cases　of　fast　motion,　occlusion　or　background　clutter,　and　the　trackers　update　tracking　models　at　each　frame　without　considering　whether　the　detection　is　accurate　or　not.　In　this　paper,　we　present　an　improved　strategy　that　is　adding　more　background　context　and　changing　the　tracker　model　updating　strategy.　Experimental　results　show　that　the　performance　of　the　model　has　been　improved　effectively.　©　2018　IEEE.