Automatic Learning of Typical Paths in Video Surveillance

Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/5358

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DC Field	Value	Language
dc.contributor.author	Vicente, Jorge	-
dc.contributor.author	Nunes, Luís	-
dc.date.accessioned	2013-07-30T14:07:36Z	-
dc.date.available	2013-07-30T14:07:36Z	-
dc.date.issued	2013-07-30	-
dc.identifier.uri	http://hdl.handle.net/10071/5358	-
dc.description.abstract	This paper presents an approach to the acquisition of typical paths tread by people in large environments, and signal deviations. This work is part of a larger project, tested both in simulations as well as using real footage. The adaptation required for the continuous update of typical paths is achieved using Self-Organizing Maps (SOM), supplemented with original solutions for map-selection.	por
dc.language.iso	eng	por
dc.rights	restrictedAccess	por
dc.subject	Self Organizing Maps	por
dc.subject	Tracking	por
dc.subject	Video	por
dc.subject	Surveillance	por
dc.title	Automatic Learning of Typical Paths in Video Surveillance	por
dc.type	conferenceObject	por
dc.event.title	XVII Jornadas de Classificação e Análise de Dados, JOCLAD 2010	por
dc.event.type	Conferência	por
dc.event.location	Lisbon, Portugal	por
dc.event.date	2010	por
dc.pagination	pp. 249-252	por
dc.publicationstatus	Publicado	por
dc.peerreviewed	Sim	por
Appears in Collections:	CTI-CRI - Comunicações a conferências internacionais

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