We　present　a　learning　model　for　object　detection　in　images　with　complex　background.　Novel　local　edge　feature　with　chamfer　distance　as　shape　comparison　measure　are　used　to　form　a　dictionary　of　templates.　The　features　can　be　calculated　very　quickly　using　the　Integral　Image　technique.　Bagging-Adaboost　algorithm　is　applied　to　select　a　discriminative　edge　features　set　and　combine　them　to　form　an　object　detector.　Floating　search　post　optimization　procedure　is　included　to　remove　base　classifiers　causing　higher　error　rates.　The　resulting　classifier　consists　of　fewer　base　classifiers　yet　achieves　better　generalization　performance.　Experimental　results　on　UCUI　image　test　sets　show　that　our　system　can　extremely　quickly　detect　objects　in　varying　conditions　(translation,　scaling,　occlusion　and　illumination)　with　high　detection　rates.　The　results　are　very　competitive　with　some　other　published　object　detection　schemes.　The　speed　of　detection　is　much　faster　than　that　of　existing　schemes.