Crowd　counting　is　challenging　due　to　unconstrained　imaging　factors,　e.g.,　background　clutters,　non-uniform　distribution　of　people,　large　scale　and　perspective　variations.　Dealing　with　these　problems　using　deep　neural　networks　requires　rich　prior　knowledge　and　multi-scale　contextual　representations.　In　this　paper,　we　propose　a　Cross-stage　Refinement　Network　(CRNet)　that　can　refine　predicted　density　maps　progressively　based　on　hierarchical　multi-level　density　priors.　In　particular,　CRNet　is　composed　of　several　fully　convolutional　networks.　They　are　stacked　together　recursively　with　the　previous　output　as　the　next　input,　and　each　of　them　serves　to　utilize　previous　density　output　to　gradually　correct　prediction　errors　of　crowd　areas　and　refine　the　predicted　density　maps　at　different　stages.　Cross-stage　multi-level　density　priors　are　further　exploited　in　our　recurrent　framework　by　the　cross-stage　skip　layers　based　on　ConvLSTM.　To　cope　with　different　challenges　of　unconstrained　crowd　scenes,　we　explore　different　crowd-specific　data　augmentation　methods　to　mimic　real-world　scenarios　and　enrich　crowd　feature　representations　from　different　aspects.　Extensive　experiments　show　the　proposed　method　achieves　superior　performances　against　state-of-the-art　methods　on　four　widely-used　challenging　benchmarks　in　terms　of　counting　accuracy　and　density　map　quality.　Code　and　models　are　available　at　this　https://github.com/lytgftyf/Crowd-Counting-via-Cross-stage-Refinement-Networks.