Self Organizing and Fuzzy Modelling for Parked Vehicles Detection (Articolo in rivista)

Type
Label
  • Self Organizing and Fuzzy Modelling for Parked Vehicles Detection (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-04697-1_39 (literal)
Alternative label
  • Maddalena, Lucia; Petrosino, Alfredo (2009)
    Self Organizing and Fuzzy Modelling for Parked Vehicles Detection
    in Lecture notes in computer science; Springer-Verlag, Berlin Heidelberg (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Maddalena, Lucia; Petrosino, Alfredo (literal)
Pagina inizio
  • 422 (literal)
Pagina fine
  • 433 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dx.doi.org/10.1007/978-3-642-04697-1_39 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Advanced Concepts for Intelligent Vision Systems (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 5807 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 5807 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Lecture Notes in Computer Science, Springer Berlin/Heidelberg, DOI: 10.1007/978-3-642-04697-1, Vol. 5807 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 12 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR Natl Res Council (literal)
Titolo
  • Self Organizing and Fuzzy Modelling for Parked Vehicles Detection (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-04696-4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (literal)
Abstract
  • Our aim is to distinguish moving and stopped objects in digital image sequences taken from stationary cameras by a model based approach. A self-organizing model is adopted both for the scene background and for the scene foreground, that can handle scenes containing moving backgrounds or gradual illumination variations, helping in distinguishing between moving and stopped foreground regions. The model is enriched by spatial coherence to enhance robustness against false detections and fuzzy modelling to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that good accuracy values can be reached for color video sequences that represent typical situations critical for vehicles stopped in no parking areas. (literal)
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