In　large-scale　surveillance　of　urban　or　rural　areas,　an　effective　placement　of　cameras　is　critical　in　maximizing　surveillance　coverage　or　minimizing　economic　cost　of　cameras.　Existing　Surveillance　Camera　Placement　(SCP)　methods　generally　focus　on　physical　coverage　of　surveillance　by　implicitly　assuming　uniform　distribution　of　interested　targets　or　objects　across　all　blocks,　which　is,　however,　uncommon　in　real-world　scenarios.　In　this　paper,　we　are　the　first　to　propose　a　target-aware　SCP　(tSCP)　model,　which　prioritizes　optimizing　the　task　based　on　uneven　target　densities,　allowing　cameras　to　preferentially　cover　blocks　with　more　interested　targets.　First,　we　define　target　density　as　the　likelihood　of　interested　targets　occurring　in　a　block,　which　is　positively　correlated　with　the　importance　of　the　block.　Second,　we　combine　aerial　imagery　with　a　lightweight　object　detection　network　to　identify　target　density.　Third,　we　formulate　tSCP　as　an　optimization　problem　to　maximize　target　coverage　in　surveillance　area,　and　solve　this　problem　with　a　target-guided　genetic　algorithm.　Our　method　optimizes　the　rational　and　economical　utilization　of　cameras　in　large-scale　video　survillance.　Compared　with　the　state-of-the-art　methods,　our　tSCP　achieves　the　highest　target　coverage　with　a　fixed　number　of　cameras　(8.31%-14.81%　more　than　its　peers),　or　utilizes　the　minimum　number　of　cameras　to　achieve　a　preset　target　coverage.　Codes　are　available　at　https://github.com/wu-hongxin/tSCP_main.