Learning Group Interactions and Semantic Intentions for Multi-Object Trajectory Prediction

TL;DR

This paper introduces a diffusion-based framework for multi-object trajectory prediction that models group interactions and semantic intentions, improving accuracy in complex scenarios like sports.

Contribution

It presents a novel diffusion model incorporating group interactions and semantic intentions, with a new dataset annotated for team tactics.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effectively models group interactions and semantic intentions

Enhances trajectory prediction accuracy in sports scenarios

Abstract

Effective modeling of group interactions and dynamic semantic intentions is crucial for forecasting behaviors like trajectories or movements. In complex scenarios like sports, agents' trajectories are influenced by group interactions and intentions, including team strategies and opponent actions. To this end, we propose a novel diffusion-based trajectory prediction framework that integrates group-level interactions into a conditional diffusion model, enabling the generation of diverse trajectories aligned with specific group activity. To capture dynamic semantic intentions, we frame group interaction prediction as a cooperative game, using Banzhaf interaction to model cooperation trends. We then fuse semantic intentions with enhanced agent embeddings, which are refined through both global and local aggregation. Furthermore, we expand the NBA SportVU dataset by adding human annotations of team-level tactics for trajectory and tactic prediction tasks. Extensive experiments on three widely-adopted datasets demonstrate that our model outperforms state-of-the-art methods. Our source code and data are available at https://github.com/aurora-xin/Group2Int-trajectory.