Learning a Pedestrian Social Behavior Dictionary

TL;DR

This paper introduces an unsupervised method to learn a semantic dictionary of pedestrian behaviors from trajectory data, enabling better understanding and prediction of pedestrian movements without manual labeling.

Contribution

It proposes a novel unsupervised framework for creating a pedestrian behavior taxonomy and demonstrates its utility in visualization and trajectory prediction tasks.

Findings

Effective behavior maps for space usage visualization

Comparable trajectory prediction results to state-of-the-art methods

Lightweight approach with low parameters

Abstract

Understanding pedestrian behavior patterns is a key component to building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are impractical since pedestrian behaviors may be unknown a priori and the process of manually generating behavior labels is prohibitively time consuming. We instead utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. We show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing the distributions of behaviors. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results comparable to SOTA on the ETH and UCY datasets.