Neural Background Subtraction for Pan-Tilt-Zoom Cameras (Articolo in rivista)

Type
Label
  • Neural Background Subtraction for Pan-Tilt-Zoom Cameras (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TSMC.2013.2280121 (literal)
Alternative label
  • Ferone, Alessio; Maddalena, Lucia (2014)
    Neural Background Subtraction for Pan-Tilt-Zoom Cameras
    in IEEE transactions on systems, man, and cybernetics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ferone, Alessio; Maddalena, Lucia (literal)
Pagina inizio
  • 571 (literal)
Pagina fine
  • 579 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 44 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 5 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Univ Naples Parthenope; Consiglio Nazionale delle Ricerche (CNR) (literal)
Titolo
  • Neural Background Subtraction for Pan-Tilt-Zoom Cameras (literal)
Abstract
  • We propose an extension of a neural-based background subtraction approach to moving object detection to the case of image sequences taken from pan-tilt-zoom (PTZ) cameras. The background model automatically adapts in a self-organizing way to changes in the scene background. Background variations arising in a usual stationary camera setting, such as those due to gradual illumination changes, to waving trees, or to shadows cast by moving objects, are accurately handled by the neural self-organizing background model originally proposed for this type of setting. Handling of variations due to the PTZ camera movement is ensured by a novel registration mechanism that allows the neural background model to automatically compensate the eventual ego-motion, estimated at each time instant. Experimental results on several real image sequences and comparisons with seven state-of-the-art methods demonstrate the accuracy of the proposed approach. (literal)
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