Publication
TranSPORTmer: A holistic approach to trajectory understanding in multi-agent sports scenarios
Conference Article
Conference
Asian Conference on Computer Vision (ACCV)
Edition
17th
Pages
333–352
Doc link
https://doi.org/10.1007/978-981-96-0901-7_20
File
Abstract
Understanding trajectories in multi-agent scenarios requires addressing diverse tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different states. Traditional data-driven approaches often handle these tasks independently, utilizing specific models tailored for individual purposes. In this paper, we introduce TranSPORTmer, a unified framework capable of simultaneously addressing all these tasks at once, showcasing its application to the intricate dynamics of multi-agent soccer and basketball scenarios. TranSPORTmer builds upon a Transformer architecture and employs Set Attention Blocks sequentially to model trajectories in an equivariant manner, effectively capturing temporal dynamics and social interactions among agents. The model's task is guided by an input mask, concealing missing or yet-to-be-predicted observations. Additionally, we introduce a CLS extra agent to classify states along soccer trajectories, including passes, possessions, out-of-play intervals, and uncontrolled states, contributing to an enhancement in modeling trajectories. To validate TranSPORTmer's efficacy, we conducted a thorough evaluation on a soccer and two basketball datasets. Across various metrics, we demonstrate that our general-purpose model consistently outperforms task-specific state-of-the-art architectures in player forecasting, unified player forecasting-imputation, ball inference, and ball imputation.
Categories
computer vision, pattern classification.
Author keywords
Multi-agent modelling, imputation, transformers.
Scientific reference
G. Capellera, L. Ferraz, A. Rubio, A. Agudo and F. Moreno-Noguer. TranSPORTmer: A holistic approach to trajectory understanding in multi-agent sports scenarios, 17th Asian Conference on Computer Vision, 2024, Hanoi, in Computer Vision – ACCV 2024, Vol 15472 of Lecture Notes in Computer Science, pp. 333–352, 2024.
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