Individuals, groups, and crowds: Modelling complex, multi-object behaviour in phase space

This paper concentrates on the problem of modelling and recognition of complex behaviours involving multi-object interactions in video. We use motion patterns of individual objects to construct models which characterize pairs by correlating them in phase space. These models of complex interactions allow for: recognition of group activities, which occur when individual people or objects start interacting as a single entity; detection of transitions from individuals to groups to crowds; and the interactions of individuals with groups, as well as the interactions of groups with other groups. We establish a general formalism by examining activities using relative distances and analyse multi-object interactions directly via the Phase Space Algorithm. Finally, we calculate a scale-invariant Group Transition Ratio to quantify formation and dispersal of both groups and crowds. Our input is solely the position information of individuals, which we get using a person tracker, optical flow, and Lagrangian particle dynamics. We demonstrate the uses of this model for recognition of complex activities on the standard CAVIAR, VIVID, and UCR Videoweb datasets.