  Shots are key narrative elements of various videos, e.g. movies, TV series,
and user-generated videos that are thriving over the Internet. The types of
shots greatly influence how the underlying ideas, emotions, and messages are
expressed. The technique to analyze shot types is important to the
understanding of videos, which has seen increasing demand in real-world
applications in this era. Classifying shot type is challenging due to the
additional information required beyond the video content, such as the spatial
composition of a frame and camera movement. To address these issues, we propose
a learning framework Subject Guidance Network (SGNet) for shot type
recognition. SGNet separates the subject and background of a shot into two
streams, serving as separate guidance maps for scale and movement type
classification respectively. To facilitate shot type analysis and model
evaluations, we build a large-scale dataset MovieShots, which contains 46K
shots from 7K movie trailers with annotations of their scale and movement
types. Experiments show that our framework is able to recognize these two
attributes of shot accurately, outperforming all the previous methods.
