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.


Noise analysis.F1 score vs. Noise Level on position (left), orientation (centre) and combined (right). (Best viewed in colour)
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123783.g008: Noise analysis.F1 score vs. Noise Level on position (left), orientation (centre) and combined (right). (Best viewed in colour)

Mentions: In particular, we produced results by adding noise on position only (leaving the orientation at its exact value), on orientation only (leaving the position of each individual at its exact value) and on both position and orientation. Fig 8 shows F1 scores for each method while increasing the noise level. In this case we can appreciate that with high orientation and combined noise IGD performs comparably or better than GCFF; this is a confirmation of the fact that methods based on Dominant Sets are performing very well when the orientation information is not reliable, as already stated in [12].


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

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

Noise analysis.F1 score vs. Noise Level on position (left), orientation (centre) and combined (right). (Best viewed in colour)
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123783.g008: Noise analysis.F1 score vs. Noise Level on position (left), orientation (centre) and combined (right). (Best viewed in colour)
Mentions: In particular, we produced results by adding noise on position only (leaving the orientation at its exact value), on orientation only (leaving the position of each individual at its exact value) and on both position and orientation. Fig 8 shows F1 scores for each method while increasing the noise level. In this case we can appreciate that with high orientation and combined noise IGD performs comparably or better than GCFF; this is a confirmation of the fact that methods based on Dominant Sets are performing very well when the orientation information is not reliable, as already stated in [12].

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.