Modeling and Recognition of Complex Multi-Person Interactions in Video

摘要

In this paper, we focus on the problem of searching for complex activities involving multiple, interacting objects in video. We examine the dynamics of formation and dispersal of groups as well as their interactions with other groups and individuals. In order to establish a general formalism, we examine activities using relative distances in phase space via pairwise analysis of all objects. This allows us to characterize interactions directly by modeling multi-object activities with the Multiple Objects, Pairwise Analysis (MOPA) feature vector, which is based upon physical models of complex interactions in phase space; specifically, we model paired motion as a damped oscillator in phase space. We model and recognize more complex interactions by characterizing pairs which are correlated in phase space as groups. We show how this model can be used for recognition of complex activities on the standard CAVIAR, VIVID, and UCR Videoweb datasets capturing a variety of problem settings.