Online Identification of Primary Social Groups

Online group identification is a challenging task, due to the inherent dynamic nature of groups. In this paper, a novel framework is proposed that combines the individual trajectories produced by a tracker along with a prediction of their evolution, in order to identify existing groups. In addition to the widely known criteria used in the literature for group identification, we present a novel one, which exploits the motion pattern of the trajectories. The proposed framework utilizes the past, present and predicted states of groups within a scene, to provide robust online group identification. Experiments were conducted to provide evidence of the effectiveness of the proposed method with promising results.