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Jardim, D., Nunes, L. & Dias, M. (2016). Impact of automated action labeling in classification of human actions in RGB-D videos. In Van Harmelen, F., Dignum, V., Dignum, F., Bouquet, P., Fox, M., Kaminka, G. A., and Hüllermeier, E. (Ed.), ECAI 2016: 22nd European Conference on Artificial Intelligence. (pp. 1632-1633). The Hage: IOS Press .

D. W. Jardim et al.,  "Impact of automated action labeling in classification of human actions in RGB-D videos", in ECAI 2016: 22nd European Conf. on Artificial Intelligence, Van Harmelen, F., Dignum, V., Dignum, F., Bouquet, P., Fox, M., Kaminka, G. A., and Hüllermeier, E., Ed., The Hage, IOS Press , 2016, vol. 285, pp. 1632-1633

@inproceedings{jardim2016_1716169921410,
	author = "Jardim, D. and Nunes, L. and Dias, M.",
	title = "Impact of automated action labeling in classification of human actions in RGB-D videos",
	booktitle = "ECAI 2016: 22nd European Conference on Artificial Intelligence",
	year = "2016",
	editor = "Van Harmelen, F., Dignum, V., Dignum, F., Bouquet, P., Fox, M., Kaminka, G. A., and Hüllermeier, E.",
	volume = "285",
	number = "",
	series = "",
	doi = "10.3233/978-1-61499-672-9-1632",
	pages = "1632-1633",
	publisher = "IOS Press ",
	address = "The Hage",
	organization = "European Association for Artificial Intelligence",
	url = "http://www.scopus.com/inward/record.url?eid=2-s2.0-85013073342&partnerID=MN8TOARS"
}

TY  - CPAPER
TI  - Impact of automated action labeling in classification of human actions in RGB-D videos
T2  - ECAI 2016: 22nd European Conference on Artificial Intelligence
VL  - 285
AU  - Jardim, D.
AU  - Nunes, L.
AU  - Dias, M.
PY  - 2016
SP  - 1632-1633
DO  - 10.3233/978-1-61499-672-9-1632
CY  - The Hage
UR  - http://www.scopus.com/inward/record.url?eid=2-s2.0-85013073342&partnerID=MN8TOARS
AB  - For many applications it is important to be able to detect what a human is currently doing. This ability is useful for applications such as surveillance, human computer interfaces, games and healthcare. 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.
ER  -