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.

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.

Although many fast stereo matching designs have been proposed in the past decades, it is still very challenging to achieve real-time speed at high definition resolution while maintaining high matching accuracy. In this paper, we propose a real-time high definition stereo matching design on FPGA. By using the Mini-Census transform and the Cross-based cost aggregation, the proposed algorithm is robust to radiometric differences and produces accurate disparity maps. The algorithm modules have been optimized for efficient hardware implementations and instantiated in an SoC environment. Implemented on a single EP3SL150 FPGA, our design achieves 60 frames per second for 1024 × 768 stereo images. Evaluated with the Middlebury stereo benchmark, the proposed design also delivers leading stereo matching accuracy among prior related work.