Visual　tracking　is　a　significant　and　challenging　task　in　computer　vision.　In　this　paper,　we　consider　visual　tracking　as　random　walks　on　ergodic　Markov　chain,　where　nodes　are　represented　as　superpixels　and　edges　represent　their　relationships.　The　graph　model　and　Markov　theory　are　integrated　to　construct　ergodic　Markov　chain.　Based　on　the　random　walks　and　introduction　of　positive　and　negative　template　nodes,　our　algorithm　can　search　candidate　nodes　belonging　to　the　target　globally　and　suppress　nodes　belonging　to　the　background.　Then　we　obtain　a　confidence　map　that　locates　target　position.　In　particular,　to　describe　patchs　more　accurately,　we　fuse　the　depth　information　into　the　representation　of　superpixels.　Furthermore,　we　construct　another　ergodic　Markov　chain　on　depth　map　to　handle　occlusion　to　make　our　algorithm　more　robust.　Experimental　results　demonstrate　that　our　algorithm　achieves　excellent　performance,　even　though　in　handing　occlusion,　non-rigid　deformation,　scale　variation,　etc.