Detection of groups of interacting people is a very interesting and useful 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 detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection 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 computational 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.

F-formation detection: Individuating free-standing conversational groups in images / Setti, F.; Russell, C.; Bassetti, C.; Cristani, M.. - In: PLOS ONE. - ISSN 1932-6203. - 10:5(2015), p. e0123783. [10.1371/journal.pone.0123783]

F-formation detection: Individuating free-standing conversational groups in images

Bassetti C.;Cristani M.
2015-01-01

Abstract

Detection of groups of interacting people is a very interesting and useful 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 detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection 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 computational 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.
2015
5
Setti, F.; Russell, C.; Bassetti, C.; Cristani, M.
F-formation detection: Individuating free-standing conversational groups in images / Setti, F.; Russell, C.; Bassetti, C.; Cristani, M.. - In: PLOS ONE. - ISSN 1932-6203. - 10:5(2015), p. e0123783. [10.1371/journal.pone.0123783]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/246822
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