Social Interpretable Tree for Pedestrian Trajectory Prediction

Understand

Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications.

• In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories.
• Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory.
• SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality.