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Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel Collective Transition prior, which leads to adoi:10.1109/cvpr.2014.285 dblp:conf/cvpr/ShaoLW14 fatcat:xeoca56c2nc5vng55cyjybbolm