Monocular camera fall detection system exploiting 3D measures

dc.contributor.author	Μακαντάσης, Κωνσταντίνος	el
dc.contributor.author	Πρωτοπαπαδάκης, Ευτύχιος Ε.	el
dc.contributor.author	Δουλάμης, Αναστάσιος Δ.	el
dc.contributor.author	Γραμματικόπουλος, Λάζαρος	el
dc.contributor.author	Στεντούμης, Χρήστος	el
dc.date.accessioned	2015-06-05T16:38:49Z
dc.date.available	2015-06-05T16:38:49Z
dc.date.issued	2015-06-05
dc.identifier.uri	http://hdl.handle.net/11400/15155
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://link.springer.com/bookseries/558	en
dc.subject	Fall detection
dc.subject	Image motion analysis
dc.subject	Self calibration
dc.subject	Semisupervised learning
dc.subject	Ανίχνευση πτώσης
dc.subject	Ανάλυση κινούμενης εικόνας
dc.subject	Αυτο-βαθμονόμηση
dc.subject	Ημι-εποπτευόμενη μάθηση
dc.title	Monocular camera fall detection system exploiting 3D measures	en
heal.type	bookChapter
heal.secondaryTitle	a semi-supervised learning approach	en
heal.generalDescription	Conference proceedings published in book series	en
heal.classification	Medicine
heal.classification	Technology
heal.classification	Ιατρική
heal.classification	Τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Τεχνολογία
heal.identifier.secondary	DOI: 10.1007/978-3-642-33885-4_9
heal.language	en
heal.access	campus
heal.recordProvider	Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Πολιτικών Μηχανικών Τ.Ε. και Μηχανικών Τοπογραφίας και Γεωπληροφορικής Τ.Ε. Κατεύθυνση Μηχανικών Τοπογραφίας & Γεωπληροφορικής Τ.Ε.	el
heal.publicationDate	2012
heal.bibliographicCitation	Makantasis, K., Protopapadakis, E., Doulamis, A., Grammatikopoulos, L. and Stentoumis, C. (2012) Monocular camera fall detection system exploiting 3D measures: a semi-supervised learning approach. In: Fusiello, A., Murino, V. and Cucchiara, R. (eds) (2012) "Computer Vision - ECCV 2012: workshops and demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part III". Berlin: Springer Berlin Heidelberg. Available from: http://link.springer.com/chapter/10.1007%2F978-3-642-33885-4_9# [Accessed: 05/06/2015].	en
heal.abstract	Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. The system presented in this article addresses the fall detection problem through visual cues. The proposed methodology utilize a fast, real-time background subtraction algorithm based on motion information in the scene and capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object and, at the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	true
heal.bookName	Computer Vision – ECCV 2012. Workshops and Demonstrations	en