  For many applications with limited computation, communication, storage and
energy resources, there is an imperative need of computer vision methods that
could select an informative subset of the input video for efficient processing
at or near real time. In the literature, there are two relevant groups of
approaches: generating a trailer for a video or fast-forwarding while
watching/processing the video. The first group is supported by video
summarization techniques, which require processing of the entire video to
select an important subset for showing to users. In the second group, current
fast-forwarding methods depend on either manual control or automatic adaptation
of playback speed, which often do not present an accurate representation and
may still require processing of every frame. In this paper, we introduce
FastForwardNet (FFNet), a reinforcement learning agent that gets inspiration
from video summarization and does fast-forwarding differently. It is an online
framework that automatically fast-forwards a video and presents a
representative subset of frames to users on the fly. It does not require
processing the entire video, but just the portion that is selected by the
fast-forward agent, which makes the process very computationally efficient. The
online nature of our proposed method also enables the users to begin
fast-forwarding at any point of the video. Experiments on two real-world
datasets demonstrate that our method can provide better representation of the
input video with much less processing requirement.
