Crowd　counting　is　a　key　problem　for　many　computer　vision　tasks　while　most　existing　methods　try　to　count　people　based　on　regression　with　hand-crafted　features.　Recently,　the　fast　development　of　deep　learning　has　resulted　in　many　promising　detectors　of　generic　object　classes.　In　this　paper,　to　effective　leverage　the　discriminability　of　convolutional　neural　networks,　we　propose　a　method　to　people　counting　based　on　Faster　R-CNN[9]　head-shoulder　detection　and　correlation　tracking.　Firstly,　we　train　a　Faster　R-CNN　head-shoulder　detector　with　Zeiler　model　to　detect　people　with　multiple　poses　and　views.　Next,　we　employ　kernelized　correlation　filter(KCF)[7]　to　track　the　people　and　obtain　the　trajectory.　Considering　the　results　of　the　detection　and　tracking,　we　fuse　the　two　bounding　box　to　obtain　a　continuous　and　stable　trajectory.　Extensive　experiments　and　comparison　show　the　promise　of　the　proposed　approach.