TrajPRed: Trajectory Prediction with Region-based Relation Learning

The core message of this work is to propose a robust trajectory prediction framework that models two major stimuli of human behavior: external social interactions and individual stochastic goals. The framework learns region-based social relations to capture the dynamics of crowd density changes, which is more robust to spatial noise perturbations compared to edge-based relation learning approaches. It also estimates multiple plausible goals to account for the stochasticity in human behavior.