Recently,　with　the　progress　of　deep　learning　technologies,　crowd　counting　has　been　rapidly　developed.　In　this　work,　we　first　introduce　a　perspective-aware　density　map　generation　method　that　is　able　to　produce　ground-truth　density　maps　from　point　annotations　to　train　crowd　counting　model　to　accomplish　superior　performance　than　prior　density　map　generation　techniques.　Besides,　leveraging　our　density　map　generation　method,　we　propose　an　iterative　distillation　algorithm　to　progressively　enhance　our　model　with　identical　network　structures.　In　experiments,　we　demonstrate　that,　with　our　simple　convolutional　neural　network　architecture　strengthened　by　our　proposed　training　algorithm,　our　model　is　able　to　outperform　or　be　comparable　with　the　state-of-the-art　methods.　©　2020　ACM.