  The Online Action Detection (OAD) problem needs to be revisited. Unlike
traditional offline action detection approaches, where the evaluation metrics
are clear and well established, in the OAD setting we find very few works and
no consensus on the evaluation protocols to be used. In this work we propose to
rethink the OAD scenario, clearly defining the problem itself and the main
characteristics that the models which are considered online must comply with.
We also introduce a novel metric: the Instantaneous Accuracy ($IA$). This new
metric exhibits an \emph{online} nature and solves most of the limitations of
the previous metrics. We conduct a thorough experimental evaluation on 3
challenging datasets, where the performance of various baseline methods is
compared to that of the state-of-the-art. Our results confirm the problems of
the previous evaluation protocols, and suggest that an IA-based protocol is
more adequate to the online scenario. The baselines models and a development
kit with the novel evaluation protocol are publicly available:
https://github.com/gramuah/ia.
