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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.


Global F1 score vs. tolerance threshold T.Between brackets in legend the Global Tolerant Matching score. Dominant Sets (DS) is averaged over 3 datasets only, because of results availability. (Best viewed in colour)
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pone.0123783.g007: Global F1 score vs. tolerance threshold T.Between brackets in legend the Global Tolerant Matching score. Dominant Sets (DS) is averaged over 3 datasets only, because of results availability. (Best viewed in colour)

Mentions: A performance analysis is also provided by changing the tolerance threshold T. Fig 7 shows the average F1 scores for each method computed over all the frames and datasets. From the curves we can appreciate how the proposed method is consistently best performing for each T-value. In the legend of Fig 7 the Global Tolerant Matching score is also reported. Again, GCFF is outperforming the state of the art, independently from the choice of T.


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

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

Global F1 score vs. tolerance threshold T.Between brackets in legend the Global Tolerant Matching score. Dominant Sets (DS) is averaged over 3 datasets only, because of results availability. (Best viewed in colour)
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123783.g007: Global F1 score vs. tolerance threshold T.Between brackets in legend the Global Tolerant Matching score. Dominant Sets (DS) is averaged over 3 datasets only, because of results availability. (Best viewed in colour)
Mentions: A performance analysis is also provided by changing the tolerance threshold T. Fig 7 shows the average F1 scores for each method computed over all the frames and datasets. From the curves we can appreciate how the proposed method is consistently best performing for each T-value. In the legend of Fig 7 the Global Tolerant Matching score is also reported. Again, GCFF is outperforming the state of the art, independently from the choice of T.

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.