Video　Based　detection　of　traffic　flow　has　great　significance　in　intelligent　transportation　systems.　For　the　low　angle　cameras,　a　novel　traffic　flow　multi-parameters　detection　method　is　proposed　in　this　paper.　Three　virtual　detecting　lines　and　a　local　background　modeling　with　adaptive　learning　rate　are　used　to　quickly　extract　vehicle　feature　points　and　eliminate　the　influence　of　activity　shadow.　Based　on　a　trained　Adaboost　(Adaptive　Boosting)　classifier,　the　feature　points　are　grouped　to　vehicles.　Then　the　grouping　errors　are　eliminated　based　on　the　motion-similarity　of　feature　points　in　tracking　process　and　the　vehicle　trajectories　are　extracted　accurately.　After　that,　the　multi-lanes　time-space　diagrams　are　generated　and　the　multi-parameters　of　traffic　flow　are　detected　automatically.　Experimental　results　prove　the　efficiency　of　the　method.　In　addition,　the　multi-lanes　time-space　diagrams　can　provide　strong　support　for　more　traffic　information　acquisition　and　more　in-depth　analysis　of　traffic　flow　characteristics.　Copyright　©　2015　by　Science　Press.