  The problem of Online Human Behaviour Recognition in untrimmed videos, aka
Online Action Detection (OAD), 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 few works and no consensus on the
evaluation protocols to be used. In this paper we introduce a novel online
metric, the Instantaneous Accuracy ($IA$), that exhibits an \emph{online}
nature, solving most of the limitations of the previous (offline) metrics. We
conduct a thorough experimental evaluation on TVSeries dataset, comparing the
performance of various baseline methods to the state of the art. Our results
confirm the problems of previous evaluation protocols, and suggest that an
IA-based protocol is more adequate to the online scenario for human behaviour
understanding. Code of the metric available https://github.com/gramuah/ia

Computer Vision and Pattern Recognition