Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction
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3688865.3689481.pdf
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Author(s)
Li, Yuke
Chen, Lixiong
Chen, Guangyi
Chan, Ching-Yao
Zhang, Kun
Anzellotti, Stefano
Wei, Donglai
Date Issued
October 28, 2024
Publisher
ACM|Proceedings of the 5th International Workshop on Human-centric Multimedia Analysis
Citation
Li, Yuke, Chen, Lixiong, Chen, Guangyi, Chan, Ching-Yao, Zhang, Kun et al. 2024. "Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction."
Version
Final published version
Abstract
In order to predict a pedestrian's trajectory in a crowd accurately, one has to take into account her/his underlying socio-temporal interactions with other pedestrians consistently. Unlike existing work that represents the relevant information separately, partially, or implicitly, we propose a complete representation for it to be fully and explicitly captured and analyzed. In particular, we introduce a Directed Acyclic Graph-based structure, which we term Socio-Temporal Graph (STG), to explicitly capture pair-wise socio-temporal interactions among a group of people across both space and time. Our model is built on a time-varying generative process, whose latent variables determine the structure of the STGs. We design an attention-based model named STGformer that affords an end-to-end pipeline to learn the structure of the STGs for trajectory prediction. Our solution achieves overall state-of-the-art prediction accuracy in two large-scale benchmark datasets. Our analysis shows that a person's past trajectory is critical for predicting another person's future path. Our model learns this relationship with a strong notion of socio-temporal localities. Statistics show that utilizing this information explicitly for prediction yields a noticeable performance gain with respect to the trajectory-only approaches.
Description
MM’24, October 28 - November 1, 2024, Melbourne, Australia
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3688865.3689481
