Impact of Automated Action Labeling in Classification of Human Actions in RGB-D Videos
; Dias, M. D.
Impact of Automated Action Labeling in Classification of Human Actions in RGB-D Videos, Proc European Conference on Artificial Intelligence , Hague, Netherlands, Vol. Volume 285: ECAI 2016, pp. 1632 - 1633, August, 2016.
Digital Object Identifier: 10.3233/978-1-61499-672-9-1632
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Human Activity Recognition (HAR) is an interdisciplinary
research area that has been attracting interest from several
research communities specialized in machine learning, computer vision,
medical and gaming research. In order to recognize a human action,
the typical approach is to use manually labeled data to perform
supervised training. This paper aims to compare the performance of
several supervised classifiers trained with manually labeled data versus
the same classifiers trained with data automatically labeled. In
this paper we propose a framework capable of recognizing human actions
using supervised classifiers trained with automatically labeled
data in RGB-D videos.