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Understanding pedestrian behavior patterns is a key component to buildingautonomous agents that can navigate among humans. We seek a learned dictionaryof pedestrian behavior to obtain a semantic description of pedestriantrajectories. Supervised methods for dictionary learning are impractical sincepedestrian behaviors may be unknown a priori and the process of manuallygenerating behavior labels is prohibitively time consuming. We instead utilizea novel, unsupervised framework to create a taxonomy of pedestrian behaviorobserved in a specific space. First, we learn a trajectory latent space thatenables unsupervised clustering to create an interpretable pedestrian behaviordictionary. We show the utility of this dictionary for building pedestrianbehavior maps to visualize space usage patterns and for computing thedistributions of behaviors. We demonstrate a simple but effective trajectoryprediction by conditioning on these behavior labels. While many trajectoryanalysis methods rely on RNNs or transformers, we develop a lightweight,low-parameter approach and show results comparable to SOTA on the ETH and UCYdatasets.

Learning a Pedestrian Social Behavior Dictionary