Recursive Social Behavior Graph for Trajectory Prediction
This work addresses trajectory prediction for pedestrians, offering a novel approach to model social interactions, though it appears incremental in its method.
The paper tackles the problem of human trajectory prediction by introducing a group-based social interaction model, which improves prediction accuracy by 11.1% in ADE and 10.8% in FDE on ETH and UCY datasets.
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.