Camera　calibration　is　a　necessary　step　to　extract　3D　metric　information　from　2D　images.　1D　object-based　camera　calibration　has　recently　received　attention　because　there　is　no　self-occlusion　problem　for　a　ID　object.　In　this　paper,　an　adaptively　weighted　1D　calibration　algorithm　with　high　accuracy　is　presented.　The　innovation　and　contributions　of　such　an　algorithm　are　two-fold:　one　is　that　data　normalization　is　used　to　improve　estimation　accuracy　of　the　relative　depths　of　marker　points　on　the　1D　calibration　object:　the　other　one　is　that　a　weighted　coefficient　is　adaptively　assigned　to　each　constraint　on　the　camera　parameters　by　analyzing　the　data　error　involved　in　the　constraint.　Experiments　with　synthetic　and　real　image　data　show　that　the　accuracy　of　the　proposed　algorithm　is　much　higher　than　that　of　classical　ID　calibration.　In　some　cases,　it　outperforms　the　nonlinear　optimal　1D　algorithm　and　is　even　comparable　with　the　normalized　2D　and　3D　calibration.　(C)　2014　Elsevier　B.V.　All　rights　reserved.