Shot　boundary　detection　is　the　first　step　of　content-based　video　retrieval.　In　this　paper,　a　novel　method　of　abrupt　cut　detection　is　proposed　based　on　motion　information　for　uncompressed　video.　First　we　analyze　motion　vector　filed　calculated　by　block-based　motion　estimation　and　extract　quantitative　angle　histogram　entropy　and　average　magnitude　of　motion　vector　filed　as　metrics　of　frame　differences,　which　respectively　describe　distribution　regularity　degree　and　intensity　of　motion　vector　field.　Then　design　adaptive　threshold　strategies　to　identify　candidate　abrupt　cuts,　finally　eliminate　false　cuts　caused　by　flashes　and　gradual　transitions　using　a　temporal　window.　Experimental　results　show　better　performance　and　higher　robustness　to　large　camera　motions　and　flashes　than　histogram-based　algorithm.　©　2011　IEEE.