Visual　tracking　is　a　fundamental　topic　in　computer　vision　and　its　core　issue　is　how　to　accurately　and　efficiently　locate　the　object　in　challenging　scenarios.　In　online　tracking,　improving　the　target　positioning　accuracy　every　frame　and　reducing　the　accumulation　of　tracking　errors　are　the　essential　goals　to　obtaining　robust　performance.　Convolutional　Neural　Network　(CNN)　based　trackers　have　shown　favorable　performance　gains　in　the　problem　of　visual　tracking　in　recent　benchmarks.　In　this　paper,　we　propose　a　novel　bounding　box　adjustment　module　for　online　visual　tracking.　By　using　a　lightweight　Siamese　neural　network　cascading　behind　a　universal　tracker,　the　confidence　of　the　target　region　on　two　dimensions　can　be　estimated　refer　to　the　reference　image,　which　allow　accurate　adjustment　of　the　object　bounding　box.　Experiments　are　performed　on　OTB-2013　challenging　benchmark　tracking　datasets　and　the　results　achieves　improvement.