  Shot boundary detection (SBD) is an important pre-processing step for video
manipulation. Here, each segment of frames is classified as either sharp,
gradual or no transition. Current SBD techniques analyze hand-crafted features
and attempt to optimize both detection accuracy and processing speed. However,
the heavy computations of optical flow prevents this. To achieve this aim, we
present an SBD technique based on spatio-temporal Convolutional Neural Networks
(CNN). Since current datasets are not large enough to train an accurate SBD
CNN, we present a new dataset containing more than 3.5 million frames of sharp
and gradual transitions. The transitions are generated synthetically using
image compositing models. Our dataset contain additional 70,000 frames of
important hard-negative no transitions. We perform the largest evaluation to
date for one SBD algorithm, on real and synthetic data, containing more than
4.85 million frames. In comparison to the state of the art, we outperform
dissolve gradual detection, generate competitive performance for sharp
detections and produce significant improvement in wipes. In addition, we are up
to 11 times faster than the state of the art.
