Occupying　the　most　significant　portion　of　global　data　traffic,　video　is　being　generated　in　almost　every　aspect　of　our　life.　Because　of　its　huge　volume,　we　are　depending　much　more　heavily　on　machine　intelligence　based　analysis.　In　the　meantime,　video　coding　technology　has　been　continuously　improved　for　better　compression　efficiency.　However,　the　state-of-the-art　video　coding　standards,　such　as　H.265/HEVC　and　versatile　video　coding　(VVC),　are　still　designed　assuming　that　the　compressed　video　will　be　watched　by　a　human　later.　Such　a　design　is　not　optimal　when　the　compressed　video　will　be　used　by　computer　vision　applications.　While　the　human　visual　system　(HVS)　is　consistently　sensitive　to　the　content　with　high　contrast,　the　impact　of　pixels　on　computer　vision　algorithms　is　task　driven.　For　example,　because　of　the　different　categories　of　objects　used　to　train　detection　algorithms,　the　influence　of　the　same　image　content　on　those　detectors　also　varies.　Therefore,　human　oriented　video　coding　strategies　may　not　be　optimal　when　the　compressed　signal　is　further　processed　by　algorithms,　as　the　encoder　is　unaware　of　the　task　specific　information.　In　this　article,　taking　object　detection　as　an　example,　we　propose　a　novel　video　coding　strategy　for　computer　vision.　By　protecting　the　information　according　to　its　importance　for　an　object　detector　rather　than　for　the　human　visual　system,　our　proposed　method　has　the　potential　to　achieve　a　better　object　detection　performance　with　the　same　bandwidth.　The　main　contributions　of　our　paper　are:　1)　the　modeling　of　the　relationship　between　object　detection　accuracy　and　bit　rate;　2)　a　back　propagation　based　method　to　analyze　the　influence　of　each　pixel　on　the　detection　of　target　objects;　3)　an　object　detection　oriented　bit　allocation　and　codec　control　parameter　determination　scheme;　4)　an　evaluation　metric　to　compare　the　impact　of　video　coding　strategies　on　a　given　object　detector　over　a　predefined　range　of　bit　rate.　Experimental　results　demonstrate　that　our　proposed　algorithm　can　better　preserve　the　video　content　vital　for　object　detection　than　state-of-the-art　video　coding　schemes.