Human　tracking　has　been　a　challenging　task　for　robot　in　the　past　decades.　In　this　paper,　to　realize　the　human　following　in　a　cluttered　environment,　a　human　tracking　system　based　on　adaptive　multi-feature　mean-shift　(AMF-MS)　under　the　double-layer　locating　mechanism　(DLLM)　is　proposed　to　solve　the　problem　of　distinguishing　target,　occlusion,　and　quick　turning.　The　DLLM,　considering　the　course　location　processing　and　fine　location　processing,　is　designed　to　estimate　the　person＇s　position　using　the　fusion　of　heterogeneous　data.　As　an　ID　tag　attached　on　target　can　be　detected　by　RF　antennas,　the　course　locating　method　can　track　the　target　easily　and　quickly.　The　Bayes　rule　is　introduced　to　calculate　the　probability　where　the　tag　exists　due　to　the　instability　of　RF　signals.　In　the　fine　locating　step,　the　AMF-MS　is　proposed　because　it　can　reduce　computational　load　and　represent　target　by　multi-feature　histogram　function.　Meanwhile,　we　combine　extended　Kalman　filter　and　AMF-MS　to　overcome　MS＇s　inability　of　occlusion.　To　control　the　robot　following　the　target　person　precisely,　an　intelligent　gear　shift　strategy　based　on　fuzzy　control　is　implemented　by　analyzing　the　robot　structure.　Experiments　demonstrate　that　the　proposed　approach　is　robust　to　handle　complex　tracking　conditions,　and　show　the　system　has　an　optimum　performance.