F-formation Detection: Individuating Free-standing Conversational Groups in Images (Rapporti tecnici/preprint/working paper)

Type
Label
  • F-formation Detection: Individuating Free-standing Conversational Groups in Images (Rapporti tecnici/preprint/working paper) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • Francesco Setti1, Chris Russell2, Chiara Bassetti1, and Marco Cristani3,4 (2014)
    F-formation Detection: Individuating Free-standing Conversational Groups in Images
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Francesco Setti1, Chris Russell2, Chiara Bassetti1, and Marco Cristani3,4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://arxiv.org/pdf/1409.2702v1.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1Institute of Cognitive Science and Technologies (ISTC), Italian National Research Council (CNR), Trento, Italy 2Department of Computer Science, University College London, UK 3Department of Computer Science, University of Verona, Italy 4Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia Genova(IIT), Italy (literal)
Titolo
  • F-formation Detection: Individuating Free-standing Conversational Groups in Images (literal)
Abstract
  • Detection of groups of interacting people is a very interesting and use- ful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a de- tailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detec- tion of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a com- putational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F- formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality. (literal)
Prodotto di
Autore CNR

Incoming links:


Prodotto
Autore CNR di
data.CNR.it