Articulated　pose　estimation　with　mixtures-of-parts　decomposes　human　body　into　several　local　component　templates　with　springs　connecting　each　other.　Such　a　method　fails　in　precisely　estimating　human　pose　especially　due　to　the　defects　of　tree　models　when　human　has　the　complicated　pose　of　body.　To　address　this　problem,　we　propose　pose　estimation　using　local　adjustment　with　mixtures-of-parts　models.　We　can　achieve　the　most　suitable　pose　of　body　by　the　blending　and　selecting　strategy　based　on　the　full　score　and　the　corresponding　attributes　of　limbs　and　body.　The　experiments　show　that　the　estimation　effect　of　human　pose　of　our　method　is　better　than　the　previous　method　based　on　articulated　pose　estimation　with　mixtures-of-parts.　©　2015　Binary　Information　Press　&　Textile　Bioengineering　and　Informatics　Society.