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首页> 外文期刊>Affective Computing, IEEE Transactions on >On Social Involvement in Mingling Scenarios: Detecting Associates of F-Formations in Still Images
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On Social Involvement in Mingling Scenarios: Detecting Associates of F-Formations in Still Images

机译：论混合情景中的社会参与：静态图像中F形的员工

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摘要

In this paper, we carry out an extensive study of social involvement in free standing conversing groups (the so-called F-formations) from static images. By introducing a novel feature representation, we show that the standard features which have been used to represent full membership in an F-formation cannot be applied to the detection of so-called associates of F-formations due to their sparser nature. We also 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. Moroever, the majority of prior works have considered the labelling of conversing groups 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 can use the detected associates to improve initial estimates of the full members of an F-formation. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on two publicly available datasets; The Idiap Poster Data and SALSA data set. Moreover, we show that parameters learned from the Idiap Poster Data can be transferred to the SALSA data, showing the power of our proposed representation in generalising over new unseen data from a different environment.
机译：在本文中，我们对来自静态图像的自由站立群体（所谓的F组）进行广泛研究。通过引入新颖的特征表示，我们表明，由于其稀疏性，不能应用于在F形成中代表F形成的完整成员资格的标准特征。我们还通过学习用于空间背景的截肢，丰富最先进的F形成建模。也就是说，F形配置相对于在混合情景期间的空间中的家具和不均匀的拥挤度的配置变化。 Moroever，大多数事先作品都认为将交谈组标记为客观任务，只需要单个注释器。但是，我们表明，通过拥抱社会参与的主观性，我们不仅在场景中产生了丰富的社交交互模型，而且可以使用检测到的员工来改善F形成的全部成员的初始估计。我们通过收集一组关于两个公共数据集的小说多元注释标签进行了广泛的实验验证了我们提出的方法; IDIAP海报数据和SALSA数据集。此外，我们表明从IDIAP海报数据学习的参数可以转移到SALSA数据，显示我们所提出的表示在来自不同环境的新未见数据中的概念。

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