TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports
This work solves trajectory understanding problems for multi-agent sports analytics, but it is incremental as it builds on existing transformer and attention methods.
The paper tackles the problem of understanding trajectories in multi-agent sports by introducing TranSPORTmer, a unified transformer-based framework that addresses multiple tasks like predicting movements and classifying states, and it outperforms state-of-the-art models in evaluations on soccer and basketball datasets.
Understanding trajectories in multi-agent scenarios requires addressing various tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different global states. Traditional data-driven approaches often handle these tasks separately with specialized models. We introduce TranSPORTmer, a unified transformer-based framework capable of addressing all these tasks, showcasing its application to the intricate dynamics of multi-agent sports scenarios like soccer and basketball. Using Set Attention Blocks, TranSPORTmer effectively captures temporal dynamics and social interactions in an equivariant manner. The model's tasks are guided by an input mask that conceals missing or yet-to-be-predicted observations. Additionally, we introduce a CLS extra agent to classify states along soccer trajectories, including passes, possessions, uncontrolled states, and out-of-play intervals, contributing to an enhancement in modeling trajectories. Evaluations on soccer and basketball datasets show that TranSPORTmer outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation. https://youtu.be/8VtSRm8oGoE