Limits...
F-formation detection: individuating free-standing conversational groups in images.

Setti F, Russell C, Bassetti C, Cristani M - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Science and Technologies (ISTC), Italian National Research Council (CNR), Trento, Italy.

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.

No MeSH data available.


Structure of an F-formation and examples of F-formation arrangements.a) Schematization of the three spaces of an F-formation: starting from the centre, o-space, p-space and r-space. b-d) Three examples of F-formation arrangements: for each one of them, one picture highlights the head and shoulder pose, the other shows the lower body posture. For a picture of circular F-formation, see also Fig 1.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4440729&req=5

pone.0123783.g004: Structure of an F-formation and examples of F-formation arrangements.a) Schematization of the three spaces of an F-formation: starting from the centre, o-space, p-space and r-space. b-d) Three examples of F-formation arrangements: for each one of them, one picture highlights the head and shoulder pose, the other shows the lower body posture. For a picture of circular F-formation, see also Fig 1.

Mentions: An F-formation arises whenever two or more people sustain a spatial and orientational relationship in which the space between them is one to which they have equal, direct, and exclusive access.


F-formation detection: individuating free-standing conversational groups in images.

Setti F, Russell C, Bassetti C, Cristani M - PLoS ONE (2015)

Structure of an F-formation and examples of F-formation arrangements.a) Schematization of the three spaces of an F-formation: starting from the centre, o-space, p-space and r-space. b-d) Three examples of F-formation arrangements: for each one of them, one picture highlights the head and shoulder pose, the other shows the lower body posture. For a picture of circular F-formation, see also Fig 1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440729&req=5

pone.0123783.g004: Structure of an F-formation and examples of F-formation arrangements.a) Schematization of the three spaces of an F-formation: starting from the centre, o-space, p-space and r-space. b-d) Three examples of F-formation arrangements: for each one of them, one picture highlights the head and shoulder pose, the other shows the lower body posture. For a picture of circular F-formation, see also Fig 1.
Mentions: An F-formation arises whenever two or more people sustain a spatial and orientational relationship in which the space between them is one to which they have equal, direct, and exclusive access.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Science and Technologies (ISTC), Italian National Research Council (CNR), Trento, Italy.

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.

No MeSH data available.