Beyond F-Formations: Determining Social Involvement in Free Standing Conversing Groups From Static Images

Lu Zhang, Hayley Hung; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1086-1095

Abstract


In this paper, we present the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images. In addition, we enrich state-of-the-art F-formation modelling by learning a frustum of attention that accounts for the spatial context. That is, F-formation configurations vary with respect to the arrangement of furniture and the non-uniform crowdedness in the space during mingling scenarios. The majority of prior works have considered the labelling of conversing group as an objective task, requiring only a single annotator. However, we show that by embracing the subjectivity of social involvement, we not only generate a richer model of the social interactions in a scene but also significantly improve F-formation detection. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on the publicly available Idiap Poster Data; the only multi-annotator labelled database of free standing conversing groups that is currently available.

Related Material


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[bibtex]
@InProceedings{Zhang_2016_CVPR,
author = {Zhang, Lu and Hung, Hayley},
title = {Beyond F-Formations: Determining Social Involvement in Free Standing Conversing Groups From Static Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}