  Egocentric action anticipation is the task of predicting the future actions a
camera wearer will likely perform based on past video observations. While in a
real-world system it is fundamental to output such predictions before the
action begins, past works have not generally paid attention to model runtime
during evaluation. Indeed, current evaluation schemes assume that predictions
can be made offline, and hence that computational resources are not limited. In
contrast, in this paper, we propose a "streaming" egocentric action
anticipation evaluation protocol which explicitly considers model runtime for
performance assessment, assuming that predictions will be available only after
the current video segment is processed, which depends on the processing time of
a method. Following the proposed evaluation scheme, we benchmark different
state-of-the-art approaches for egocentric action anticipation on two popular
datasets. Our analysis shows that models with a smaller runtime tend to
outperform heavier models in the considered streaming scenario, thus changing
the rankings generally observed in standard offline evaluations. Based on this
observation, we propose a lightweight action anticipation model consisting in a
simple feed-forward 3D CNN, which we propose to optimize using knowledge
distillation techniques and a custom loss. The results show that the proposed
approach outperforms prior art in the streaming scenario, also in combination
with other lightweight models.
