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.


Examples of F-formations.a) in orange, the o-space; b) an aerial image of a circular F-formation; c) a party, something similar to a typical surveillance setting with the camera located 2–3 meters from the floor: detecting F-formations here is challenging.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123783.g001: Examples of F-formations.a) in orange, the o-space; b) an aerial image of a circular F-formation; c) a party, something similar to a typical surveillance setting with the camera located 2–3 meters from the floor: detecting F-formations here is challenging.

Mentions: In Kendon’s terms [8, 24, 25], an F-formation is a socio-spatial formation in which people have established and maintain a convex space (called o-space) to which everybody in the gathering has direct, easy and equal access. Typically, people arrange themselves in the form of a circle, ellipse, horseshoe, side-by-side or L-shape (cf. Section Method), so that they can have easy and preferential access to one another while excluding distractions of the outside world with their backs. Examples of F- formations are reported in Fig 1. In computer vision, spatial position and orientational information can be automatically extracted, and these facts pave the way to the computational modeling of F-formation and, as a consequence, of the FCGs.


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

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

Examples of F-formations.a) in orange, the o-space; b) an aerial image of a circular F-formation; c) a party, something similar to a typical surveillance setting with the camera located 2–3 meters from the floor: detecting F-formations here is challenging.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123783.g001: Examples of F-formations.a) in orange, the o-space; b) an aerial image of a circular F-formation; c) a party, something similar to a typical surveillance setting with the camera located 2–3 meters from the floor: detecting F-formations here is challenging.
Mentions: In Kendon’s terms [8, 24, 25], an F-formation is a socio-spatial formation in which people have established and maintain a convex space (called o-space) to which everybody in the gathering has direct, easy and equal access. Typically, people arrange themselves in the form of a circle, ellipse, horseshoe, side-by-side or L-shape (cf. Section Method), so that they can have easy and preferential access to one another while excluding distractions of the outside world with their backs. Examples of F- formations are reported in Fig 1. In computer vision, spatial position and orientational information can be automatically extracted, and these facts pave the way to the computational modeling of F-formation and, as a consequence, of the FCGs.

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.