  Detection of video shot transition is a crucial pre-processing step in video
analysis. Previous studies are restricted on detecting sudden content changes
between frames through similarity measurement and multi-scale operations are
widely utilized to deal with transitions of various lengths. However,
localization of gradual transitions are still under-explored due to the high
visual similarity between adjacent frames. Cut shot transitions are abrupt
semantic breaks while gradual shot transitions contain low-level
spatial-temporal patterns caused by video effects in addition to the gradual
semantic breaks, e.g. dissolve. In order to address the problem, we propose a
structured network which is able to detect these two shot transitions using
targeted models separately. Considering speed performance trade-offs, we design
a smart framework. With one TITAN GPU, the proposed method can achieve a
30\(\times\) real-time speed. Experiments on public TRECVID07 and RAI databases
show that our method outperforms the state-of-the-art methods. In order to
train a high-performance shot transition detector, we contribute a new database
ClipShots, which contains 128636 cut transitions and 38120 gradual transitions
from 4039 online videos. ClipShots intentionally collect short videos for more
hard cases caused by hand-held camera vibrations, large object motions, and
occlusion.
