TrajFine: Predicted Trajectory Refinement for Pedestrian Trajectory Forecasting

Kuan-Lin Wang, Li-Wu Tsao, Jhih-Ciang Wu, Hong-Han Shuai, Wen-Huang Cheng

Abstract: Trajectory prediction, aiming to forecast future trajectories based on past ones, encounters two pivotal issues: insufficient interactions and scene incompetence. The former signifies a lack of consideration for the interactions of predicted future trajectories among agents, resulting in a potential collision, while the latter indicates the incapacity for learning complex social interactions from simple data. To establish an interaction-aware approach, we propose a diffusion-based model named TrajFine to extract social relationships among agents and refine predictions by considering past predictions and future interactive dynamics. Additionally, we introduce Scene Mixup to facilitate the augmentation via integrating agents from distinct scenes under the Curriculum Learning strategy, progressively increasing the task difficulty during training. Extensive experiments demonstrate the effectiveness of TrajFine for trajectory forecasting by outperforming current SOTAs with significant improvements on the benchmarks.