Masked autoencoder for multiagent trajectories

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

Automatically labeling trajectories of multiple agents is key to behavioral analyses but usually requires a large amount of manual annotations. This also applies to the domain of team sport analyses. In this paper, we specifically show how pretraining transformer models improves the classification performance on tracking data from professional soccer. For this purpose, we propose a novel self-supervised masked autoencoder for multiagent trajectories to effectively learn from only a few labeled sequences. Our approach builds upon a factorized transformer architecture for multiagent trajectory data and employs a masking scheme on the level of individual agent trajectories. As a result, our model allows for a reconstruction of masked trajectory segments while being permutation equivariant with respect to the agent trajectories. In addition to experiments on soccer, we demonstrate the usefulness of the proposed pretraining approach on multiagent pose data from entomology. In contrast to related work, our approach is conceptually much simpler, does not require handcrafted features and naturally allows for permutation invariance in downstream tasks.
OriginalspracheEnglisch
Aufsatznummer44
ZeitschriftMachine Learning
Jahrgang114
Ausgabenummer2
Anzahl der Seiten18
ISSN0885-6125
DOIs
PublikationsstatusErschienen - 02.2025

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© The Author(s) 2025.

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