统一框架同时解决体育场景中轨迹预测、补全与状态识别问题
TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports
- 用集合注意力块建模多智能体时序交互，保持对称性不变
- 在足球和篮球数据集上超越专用模型，提升轨迹预测与补全精度
- 适合研究复杂运动场景建模的学者和体育数据分析从业者
多智能体场景中的轨迹理解涉及未来运动预测、缺失观测补全、未见智能体状态推断及全局状态分类等多个任务。传统方法通常采用专用模型分别处理。我们提出TranSPORTmer，一种基于Transformer的统一框架，可同时应对上述任务，应用于足球、篮球等复杂多智能体体育场景。通过Set Attention Blocks，模型以等变方式捕捉时序动态与社交交互。输入掩码引导模型处理缺失或待预测信息，额外引入CLS代理用于分类足球轨迹中的传球、控球、无控状态及出界时段。在足球与篮球数据集上的评估显示，TranSPORTmer在球员预测、预测-补全、球体推断与球体补全任务上均优于现有最先进模型。
原文摘要 · Abstract (English)
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
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