  Social interaction is an important topic in human trajectory prediction to
generate plausible paths. In this paper, we present a novel insight of
group-based social interaction model to explore relationships among
pedestrians. We recursively extract social representations supervised by
group-based annotations and formulate them into a social behavior graph, called
Recursive Social Behavior Graph. Our recursive mechanism explores the
representation power largely. Graph Convolutional Neural Network then is used
to propagate social interaction information in such a graph. With the guidance
of Recursive Social Behavior Graph, we surpass state-of-the-art method on ETH
and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully
predict complex social behaviors.
