论文标题
NBA球员的多模式轨迹预测
Multi-Modal Trajectory Prediction of NBA Players
论文作者
论文摘要
国家篮球协会（NBA）的球员是高度积极进取的专家，可以在比赛过程中的每个时间点解决复杂的决策问题。为了了解玩家如何做出决策的一步，我们将重点放在比赛中的运动轨迹上。我们提出了一种捕获玩家的多模式行为的方法，他们可能会考虑多个轨迹并选择最有利的轨迹。该方法建立在基于LSTM的架构上，该体系结构可预测多个轨迹及其概率，该轨迹由多模式损耗功能训练，该功能更新了最佳的轨迹。大型，细粒度的NBA跟踪数据的实验表明，所提出的方法的表现优于最先进的方法。此外，结果表明该方法会产生更现实的轨迹，并且可以学习特定玩家的单个玩游戏风格。
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their decisions, we focus on their movement trajectories during games. We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function that updates the best trajectories. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art. In addition, the results indicate that the approach generates more realistic trajectories and that it can learn individual playing styles of specific players.
