Ball detection in static images with Support Vector Machines for classification (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Ball detection in static images with Support Vector Machines for classification (Articolo in rivista) (literal)

Anno

• 2003-01-01T00:00:00+01:00 (literal)

Alternative label

• N. Ancona 1, G. Cicirelli 1, E. Stella 1 and A. Distante 1 (2003)
  Ball detection in static images with Support Vector Machines for classification
  in Image and vision computing
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• N. Ancona 1, G. Cicirelli 1, E. Stella 1 and A. Distante 1 (literal)

Pagina inizio

• 675 (literal)

Pagina fine

• 692 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 21 (literal)

Rivista

• Image and vision computing (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto

• Pubblicazione su rivista internazionale. (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1-CNR (literal)

Titolo

• Ball detection in static images with Support Vector Machines for classification (literal)

Abstract

• We present a general method for detecting balls in images at the aim of automatically detecting goals during a soccer match. The detector learns the object to detect by using a supervised learning scheme called Support Vector Machines, in which the examples are views of the object. Due to the attitude of the camera with respect to football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a soccer match. Numerous theoretical and practical issues are addressed in the paper. The first one concerns the determination of negative examples relevant for the problem at hand and the training of a reference classifier in the case of an unbalanced number of positive and negative examples. The second one focuses on the reduction of the computational complexity of the reference classifier during the test phase, without increasing its generalization error. The third issue regards the problem of parameter selection, which is equivalent, in our context, to the problem of selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. Experimental results on real images show the performances of the proposed detection scheme. (literal)

Prodotto di

• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR

• GRAZIA CICIRELLI (Persona)
• NICOLA ANCONA (Unità di personale interno)
• ETTORE STELLA (Unità di personale interno)
• ARCANGELO DISTANTE (Persona)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR di

• ETTORE STELLA (Unità di personale interno)
• ARCANGELO DISTANTE (Persona)
• NICOLA ANCONA (Unità di personale interno)
• GRAZIA CICIRELLI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Image and vision computing (Rivista)