From ego to nos-vision: Detecting social relationships in first-person views

In this paper we present a novel approach to detect groups in ego-vision scenarios. People in the scene are tracked through the video sequence and their head pose and 3D location are estimated. Based on the concept of f-formation, we define with the orientation and distance an inherently social pairwise feature that describes the affinity of a pair of people in the scene. We apply a correlation clustering algorithm that merges pairs of people into socially related groups. Due to the very shifting nature of social interactions and the different meanings that orientations and distances can assume in different contexts, we learn the weight vector of the correlation clustering using Structural SVMs. We extensively test our approach on two publicly available datasets showing encouraging results when detecting groups from first-person camera views.