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	Author	Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos
	Title	A predictive model for human activity recognition by observing actions and context			Type	Conference Article
	Year	2015	Publication	Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015	Abbreviated Journal
	Volume	9386	Issue		Pages	323-333
	Keywords
	Abstract	This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
	Address	Catania; Italy; October 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-25902-4	Medium
	Area		Expedition		Conference	ACIVS
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ RFS2015			Serial	2661
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