Masked autoencoder for multiagent trajectories

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 p...

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Veröffentlicht in:Machine learning Jg. 114; H. 2; S. 44
Hauptverfasser: Rudolph, Yannick, Brefeld, Ulf
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.02.2025
Springer Nature B.V
Schlagworte:
Annotations
Artificial Intelligence
Behavior
Classification
Computer Science
Control
Entomology
Machine Learning
Mechatronics
Multiagent systems
Natural Language Processing (NLP)
Permutations
Professional soccer
Robotics
Simulation and Modeling
Soccer
Special Issue on Machine Learning in Soccer
Transformer
Tracking data
Masked autoencoder
Soccer
Multiagent trajectories
Self-supervised learning
ISSN:0885-6125, 1573-0565
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-024-06647-3