Vision　based　human　motion　tracking　has　drawn　considerable　interests　recently　because　of　its　extensive　applications.　In　this　paper,　we　propose　an　approach　to　tracking　the　body　motion　of　human　balancing　on　each　foot.　The　ability　to　balance　properly　is　an　important　indication　of　neurological　condition.　Comparing　with　many　other　human　motion　tracking,　there　is　much　less　occlusion　in　human　balancing　tracking.　This　less　constrained　problem　allows　us　to　combine　a　2D　model　of　human　body　with　image　analysis　techniques　to　develop　an　efficient　motion　tracking　algorithm.　First　we　define　a　hierarchical　2D　model　consisting　of　six　components　including　head,　body　and　four　limbs.　Each　of　the　four　limbs　involves　primary　component　(upper　arms　and　legs)　and　secondary　component　(lower　arms　and　legs)　respectively.　In　this　model,　we　assume　each　of　the　components　can　be　represented　by　quadrangles　and　every　component　is　connected　to　one　of　others　by　a　joint.　By　making　use　of　inherent　correlation　between　different　components,　we　design　a　top-　down　updating　framework　and　an　adaptive　algorithm　with　constraints　of　foreground　regions　for　robust　and　efficient　tracking.　The　approach　has　been　tested　using　the　balancing　movement　in　HumanEva-　I/　II　dataset.　The　average　tracking　time　is　under　one　second,　which　is　much　shorter　than　most　of　current　schemes.