  Action anticipation aims to detect an action before it happens. Many real
world applications in robotics and surveillance are related to this predictive
capability. Current methods address this problem by first anticipating visual
representations of future frames and then categorizing the anticipated
representations to actions. However, anticipation is based on a single past
frame's representation, which ignores the history trend. Besides, it can only
anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED)
network for action anticipation. RED takes multiple history representations as
input and learns to anticipate a sequence of future representations. One
salient aspect of RED is that a reinforcement module is adopted to provide
sequence-level supervision; the reward function is designed to encourage the
system to make correct predictions as early as possible. We test RED on
TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation
and achieve state-of-the-art performance on all datasets.
