Social Interpretable Tree for Pedestrian Trajectory Prediction

TL;DR

The paper introduces a Social Interpretable Tree (SIT) method for pedestrian trajectory prediction that explicitly models multiple future behaviors with a hand-crafted tree structure, offering interpretability and competitive performance.

Contribution

It proposes a novel tree-based approach for multi-modal trajectory prediction that enhances interpretability and matches state-of-the-art accuracy without relying on deep neural networks.

Findings

The hand-crafted tree outperforms many neural network methods.

The method achieves comparable or better results on ETH-UCY and Stanford Drone datasets.

The approach offers flexibility in long-term and diverse predictions.

Abstract

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. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-$K$ predictions.