For　moving　cameras,　the　video　content　changes　significantly,　which　leads　to　inaccurate　prediction　in　traditional　inter　prediction　and　results　in　limited　compression　efficiency.　To　solve　these　problems,　first,　we　propose　a　camera　pose-based　background　modeling　(CP-BM)　framework　that　uses　the　camera　motion　and　the　textures　of　reconstructed　frames　to　model　the　background　of　the　current　frame.　Compared　with　the　reconstructed　frames,　the　predicted　background　frame　generated　by　CP-BM　is　more　geometrically　similar　to　the　current　frame　in　position　and　is　more　strongly　correlated　with　it　at　the　pixel　level;　thus,　it　can　serve　as　a　higher-quality　reference　for　inter　prediction,　and　the　compression　efficiency　can　be　improved.　Second,　to　compensate　the　motion　of　the　background　pixels,　we　construct　a　pixel-level　motion　vector　field　that　can　accurately　describe　various　complex　motions　with　only　a　small　overhead.　Our　method　is　more　general　than　other　motion　models　because　it　has　more　degrees　of　freedom,　and　when　the　degrees　of　freedom　are　decreased,　it　encompasses　other　motion　models　as　special　cases.　Third,　we　propose　an　optical　flow-based　depth　estimation　(OF-DE)　method　to　synchronize　the　depth　information　at　the　codec,　which　is　used　to　build　the　motion　vector　field.　Finally,　we　integrate　the　overall　scheme　into　the　High　Efficiency　Video　Coding　(HEVC)　and　Versatile　Video　Coding　(VVC)　reference　software　HM-16.7　and　VTM-10.0.　Experimental　results　demonstrate　that　in　HM-16.7,　for　in-vehicle　video　sequences,　our　solution　has　an　average　Bj　&　oslash;ntegaard　delta　bit　rate　(BD-rate)　gain　of　8.02%　and　reduces　the　encoding　time　by　20.9%　due　to　the　superiority　of　our　scheme　in　motion　estimation.　Moreover,　in　VTM-10.0　with　affine　motion　compensation　(MC)　turned　off　and　turned　on,　our　method　has　average　BD-rate　gains　of　5.68%　and　0.56%,　respectively.