Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloStopped Object Detection by Learning Foreground Model in Videos
Anno di pubblicazione2013
FormatoCartaceo
Autore/iL. Maddalena/A. Petrosino
Affiliazioni autoriICAR-CNR/University Parthenope of Naples
Autori CNR e affiliazioni
  • LUCIA MADDALENA
Lingua/e
  • inglese
AbstractThe automatic detection of objects that are abandoned or removed in a video scene is an interesting area of computer vision, with key applications in video surveillance. Forgotten or stolen luggage in train and airport stations and irregularly parked vehicles are examples that concern significant issues, such as the fight against terrorism and crime, and public safety. Both issues involve the basic task of detecting static regions in the scene. We address this problem by introducing a model-based framework to segment static foreground objects against moving foreground objects in single view sequences taken from stationary cameras. An image sequence model, obtained by learning in a self-organizing neural network image sequence variations, seen as trajectories of pixels in time, is adopted within the model-based framework. Experimental results on real video sequences and comparisons with existing approaches show the accuracy of the proposed stopped object detection approach.
Lingua abstractinglese
Altro abstract-
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Pagine da723
Pagine a735
Pagine totali13
RivistaIEEE Transactions on Neural Networks and Learning Systems
Attiva dal 2012
Editore: Institute of Electrical and Electronics Engineers, - New York, NY - USA
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2162-237X
Titolo chiave: IEEE Transactions on Neural Networks and Learning Systems
Numero volume della rivista24
Fascicolo della rivista5
DOI10.1109/TNNLS.2013.2242092
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000316494700004)
  • Scopus (Codice:2-s2.0-84884969879)
Parole chiaveArtificial neural network, image sequence modeling, stopped foreground detection, video surveillance
Link (URL, URI)http://dx.doi.org/10.1109/TNNLS.2013.2242092
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Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
Moduli/Attività/Sottoprogetti CNR-
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Articolo pubblicato (documento privato )
Tipo documento: application/pdf