An unsupervised group detection method for understanding group dynamics in crowds

Abstract

Pedestrian groups arrive in large numbers in crowd gatherings, especially of a spiritual nature. Various studies have been done on crowd control in public spaces by analysing the behaviour of pedestrian groups. Understanding group dynamics can help better plan pedestrian facilities and large events. Many existing group sensing models primarily determine social bonding between pedestrians using spatiotemporal parameters, such as distance, directional movement, and overlapping time. However, social bonding determined based on these parameters assumes the bonding to be symmetric, spatially and temporally static and is unaffected by neighbourhood. Our study addresses the issue by relaxing such assumptions and developing an unsupervised group detection model based on potential candidates. The proposed model can handle temporal and spatial variations more effectively than those based on simple spatiotemporal parameters. The model developed is assessed both quantitatively and qualitatively. New metrics are introduced for quantitative evaluation, comparing predicted groups and ground truth instead of pedestrian pairs with ground truth. A visualisation method is developed for the qualitative assessment. Group splits and group merges are calculated to assist in understanding crowd movement patterns. Overall, this study helps in further exploring and assessing groups, which can improve understanding of crowd dynamics.