A multiview-based parameter free framework for group detection

Group detection is fundamentally important for analyzing crowd behaviors, and has attracted plenty of attention in artificial intelligence. However, existing works mostly have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this paper, we propose the Multiview-based Parameter Free (MPF) approach to detect groups in crowd scenes. The main contributions made in this study are threefold: (1) a new structural context descriptor is designed to characterize the structural property of individuals in crowd motions; (2) an self-weighted multiview clustering method is proposed to cluster feature points by incorporating their motion and context similarities; (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. Extensive experiments on various real world datasets demonstrate the effectiveness of the proposed approach, and show its superiority against state-of-the-art group detection techniques.