This　paper　presents　a　person　detection　and　tracking　method　for　a　mobile　robot　by　fusing　the　data　from　Radio　Frequency　Identification　(RFID)　and　stereo　camera.　The　RFID　system　detects　a　person　wearing　an　ID　tag　and　a　course　position　estimate　of　the　person　is　obtained.　The　stereo　camera　is　used　for　person　detection　based　on　the　compressive　sensing　theory.　Less　Haar-like　features　are　extracted　from　compressed　domain　to　represent　the　person　by　a　sparse　measurement　matrix.　Furthermore,　an　improved　Bayesian　classifier　is　presented　to　handle　appearance　changes　caused　by　illumination,　pose,　occlusion　and　motion　in　the　tracking　process.　The　detections　from　the　RFID　and　the　stereo　camera　are　fused　to　obtain　the　final　position　which　will　be　passed　onto　the　controller　of　the　robot.　An　Intelligent　Gear　Shift　Control　strategy　is　presented　to　adjust　the　robot＇s　linear　velocity　and　turning　radius　automatically　to　follow　the　detected　person.　The　experimental　results　show　that　the　presented　method　performs　well　in　terms　of　robustness　and　efficiency.