  A group of persons can be analyzed at various semantic levels such as
individual actions, their interactions, and the activity of the entire group.
In this paper, we propose a structural recurrent neural network (SRNN) that
uses a series of interconnected RNNs to jointly capture the actions of
individuals, their interactions, as well as the group activity. While previous
structural recurrent neural networks assumed that the number of nodes and edges
is constant, we use a grid pooling layer to address the fact that the number of
individuals in a group can vary. We evaluate two variants of the structural
recurrent neural network on the Volleyball Dataset.
