Nowadays,　digital　videos　have　been　widely　leveraged　to　record　and　share　various　events　and　people’s　daily　life.　It　becomes　urgent　to　provide　automatic　video　semantic　analysis　and　management　for　convenience.　Shot　boundary　detection　(SBD)　plays　a　key　fundamental　role　in　various　video　analysis.　Shot　boundary　detection　aims　to　automatically　detecting　boundary　frames　of　shots　in　videos.　In　this　paper,　we　propose　a　progressive　method　for　shot　boundary　detecting　with　histogram　based　shot　filtering　and　C3D　based　gradual　shot　detection.　Abrupt　shots　were　detected　firstly　for　its　specialty　and　help　alleviate　locating　shots　across　different　shots　by　dividing　the　whole　video　into　segments.　Then,　over　the　segments,　gradual　shot　detection　is　implemented　via　a　three-dimensional　convolutional　neural　network　model,　which　assign　video　clips　into　shot　types　of　normal,　dissolve,　foi　or　swipe.　Finally,　for　untrimmed　videos,　a　frame　level　merging　strategy　is　constructed　to　help　locate　the　boundary　of　shots　from　neighboring　frames.　The　experimental　results　demonstrate　that　the　proposed　method　can　effectively　detect　shots　and　locate　their　boundaries.　©　Springer　Nature　Switzerland　AG　2018.