While important advances were recently made towards temporally localizing and
recognizing specific human actions or activities in videos, efficient detection
and classification of long video chunks belonging to semantically defined
categories such as "pursuit" or "romance" remains challenging.We introduce a
new dataset, Action Movie Franchises, consisting of a collection of Hollywood
action movie franchises. We define 11 non-exclusive semantic categories -
called beat-categories - that are broad enough to cover most of the movie
footage. The corresponding beat-events are annotated as groups of video shots,
possibly overlapping.We propose an approach for localizing beat-events based on
classifying shots into beat-categories and learning the temporal constraints
between shots. We show that temporal constraints significantly improve the
classification performance. We set up an evaluation protocol for beat-event
localization as well as for shot classification, depending on whether movies
from the same franchise are present or not in the training data
