Mean　Shift　is　widely　concerned　because　of　its　advantages　like　fast　convergence,　real-time　performance　and　simple　procedure.　However,　the　tracking　performance　of　the　traditional　Mean　Shift　algorithm　is　obviously　interfered　when　the　background　has　the　similar　color　as　the　target　or　the　illumination　changes.　Besides,　the　tracking　is　easy　to　defeat　in　the　case　of　target　occlusion　and　loss　of　important　frames.　Therefore,　this　paper　proposes　two　improvements　based　on　the　traditional　Mean　Shift　tracking　algorithm.　First,　the　HLBP　texture　feature　and　color　feature　are　employed　to　describe　the　target　feature　in　order　to　improve　the　robustness　of　the　tracking　algorithm.　Second,　multiple　models　are　taken　into　account　to　provide　more　abundant　choices　for　the　tracking　process　which　can　improve　the　tacking　performance.　The　result　of　experiments　show　that　our　algorithm　is　more　robust　under　the　case　of　object　occlusion　and　posture　change,　and　gets　better　performance　in　accuracy　when　the　background　color　and　target　color　are　similar　or　the　illumination　changes.　©　2019　Association　for　Computing　Machinery.