Counting　the　number　of　people　and　estimating　their　walking　speeds　are　essential　in　crowd　control　and　flow.　In　this　work,　we　propose　a　system　that　uses　prevalent　Wi-Fi　signals　to　identify　the　number　of　people　entering　and　leaving　a　room　through　a　door.　It　selects　the　best　subcarrier　of　Wi-Fi　signals　and　applies　the　Hampel　filter　to　remove　outlier　information　first.　Then,　it　employs　a　double　threshold　method　to　determine　the　start　and　end　times　of　entering　or　leaving.　Afterward,　it　compares　the　detected　signals　with　the　precollected　database　using　the　dynamic　time-warping　algorithm　and　determines　the　number　of　people.　It　uses　a　variance　threshold　method　to　identify　the　states　of　entering　or　leaving.　It　also　employs　a　nonlinear　fitting　approach　to　calculate　the　walking　speeds.　The　experiments　show　that,　in　a　large　empty　laboratory,　the　accuracy　rates　in　determining　the　number　of　people　are　100%　for　one　person,　81%　for　two　persons,　and　95%　for　three　persons.　In　a　small　office,　the　accuracy　rates　for　detecting　the　number　of　people　are　98%　for　one　or　two　persons,　82%　for　three　persons,　93%　for　four,　and　75%　for　five　persons.　For　the　walking　speed　estimation,　the　accuracy　rate　for　a　speed　error　of　less　than　0.2410　m/s　is　75%　for　a　single　person.