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The problem of Online Human Behaviour Recognition in untrimmed videos, akaOnline Action Detection (OAD), needs to be revisited. Unlike traditionaloffline action detection approaches, where the evaluation metrics are clear andwell established, in the OAD setting we find few works and no consensus on theevaluation protocols to be used. In this paper we introduce a novel onlinemetric, the Instantaneous Accuracy ($IA$), that exhibits an \emph{online}nature, solving most of the limitations of the previous (offline) metrics. Weconduct a thorough experimental evaluation on TVSeries dataset, comparing theperformance of various baseline methods to the state of the art. Our resultsconfirm the problems of previous evaluation protocols, and suggest that anIA-based protocol is more adequate to the online scenario for human behaviourunderstanding. Code of the metric available https://github.com/gramuah/ia

The Instantaneous Accuracy: a Novel Metric for the Problem of Online Human Behaviour Recognition in Untrimmed Videos