In　recent　years,　Siamese　trackers　have　been　extensively　studied　because　of　their　high　accuracy　and　speed.　However,　when　the　target　is　occluded　by　other　objects,　the　result　will　be　greatly　drifted,　which　affects　the　quality　of　the　tracking　results.　This　study　based　on　the　RGB-D　data　proposes　an　object　tracking　method　integrating　a　target　occlusion　estimation　module　and　a　target　location　correction　module,　called　Siamese-Occlusion-Correction　(SiamOC).　When　a　target　is　occluded,　these　modules　can　help　the　Siamese　tracker　correct　the　target　location.　In　this　paper,　experiments　demonstrate　that　the　method　which　is　the　real-time　tracker　has　achieved　competitive　results　on　the　CDTB　dataset.