Detecting　and　tracking　people　are　challenging　problems,　because　the　human　body　is　non-rigid　and　the　detected　human　are　easily　occluded　by　other　objects.　In　this　paper,　we　present　a　robust　human　detecting　and　tracking　system　which　can　be　used　in　indoor　environments.　The　proposed　method　is　to　get　the　human＇s　disparity　image　from　stereo　camera,　and　then　we　extract　the　identify　model　human　of　by　image　processing.　Hu　moment　is　chosen　to　detect　human　because　it　has　the　invariant　character　of　translation,　rotation,　proportion.　Robust　human　tracking　is　performed　with　Extend　Kalman　Filter　(EKF)　as　it＇s　flexible　and　easy　to　apply　in　practical　environments.　In　proposed　system,　the　operator　can　monitor　the　process;　modify　the　parameters　and　observe　the　experiment　results　in　developed　interactive　GUI.　Our　experiments　have　demonstrated　that　the　method　improves　human　detecting　and　tracking　robustness.　©　2011　IEEE.