A new approach for social group detection based on spatio-temporal interpersonal distance measurement.

Visual-based social group detection aims to cluster pedestrians in crowd scenes according to social interactions and spatio-temporal position relations by using surveillance video data. It is a basic technique for crowd behaviour analysis and group-based activity understanding. According to the theory of proxemics study, the interpersonal relationship between individuals determines the scope of their self-space, while the spatial distance can reflect the closeness degree of their interpersonal relationship. In this paper, we proposed a new unsupervised approach to address the issues of interaction recognition and social group detection in public spaces, which remits the need to intensely label time-consuming training data. First, based on pedestrians' spatio-temporal trajectories, the interpersonal distances among individuals were measured from static and dynamic perspectives. Combined with proxemics' theory, a social interaction recognition scheme was designed to judge whether there is a social interaction between pedestrians. On this basis, the pedestrians are clustered to identify if they form a social group. Extensive experiments on our pedestrian dataset "SCU-VSD-Social" annotated with multi-group labels demonstrated that the proposed method has outstanding performance in both accuracy and complexity.