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

Autor:	Rios, Marcos Baptista, López-Sastre, Roberto J., Heilbron, Fabian Caba, van Gemert, Jan, Acevedo-Rodríguez, Francisco Javier, Maldonado-Bascón, Saturnino
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 Comment: Published at ICCV 2019 workshop: Human Behaviour Understanding
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.09970 Zobrazit plný text záznamu View this record from Arxiv