The　task　of　object　tracking　is　to　calculate　the　location　of　the　selected　object　in　the　video　sequence,　but　it　will　face　the　problem　of　performance　reduction　in　the　noise　environment.　The　goal　of　this　paper　is　to　enhance　the　noise　resistance　of　the　tracking　network　without　increasing　the　computational　burden.　The　Siamese　Octave　convolution　module　is　proposed　to　improve　the　noise　resistance　of　CNN　features　and　to　further　enhance　the　self-similarity　of　template　feature　map　and　search　feature　map.　At　the　same　time,　a　new　cross-correlation　fusion　method　is　introduced,　which　improves　the　multi-scale　of　cross-correlation　results　by　aggregating　local　and　global　context　information,　and　brings　better　adaptability　at　various　noise　levels.　Through　the　use　of　our　method　on　the　anchor-free　tracker　Ocean,　the　experimental　results　show　that　the　improved　method　proposed　in　this　paper　can　effectively　improves　the　noise　resistance　of　the　object　tracking　model.　©　2021　IEEE.