  Summarizing video content is an important task in many applications. This
task can be defined as the computation of the ordered list of actions present
in a video. Such a list could be extracted using action detection algorithms.
However, it is not necessary to determine the temporal boundaries of actions to
know their existence. Moreover, localizing precise boundaries usually requires
dense video analysis to be effective. In this work, we propose to directly
compute this ordered list by sparsely browsing the video and selecting one
frame per action instance, task known as action spotting in literature. To do
this, we propose ActionSpotter, a spotting algorithm that takes advantage of
Deep Reinforcement Learning to efficiently spot actions while adapting its
video browsing speed, without additional supervision. Experiments performed on
datasets THUMOS14 and ActivityNet show that our framework outperforms state of
the art detection methods. In particular, the spotting mean Average Precision
on THUMOS14 is significantly improved from 59.7% to 65.6% while skipping 23% of
video.
