arXiv cs.AI· Marc Guti\'errez-P\'erez, Antonio Agudo·· 11 小时前AI 评分24
基于跟踪数据的分位数回归学习人类运动可行性
Learning Human Sports Motion Feasibility via Quantile Regression on Tracking Data
AI 导读
研究将体育中的到达时间（TTA）建模为条件分位数回归问题，估计移动时间的下分位数以学习球员的下包络到达时间。模型在 PFF FC FIFA World Cup 2022 全部 64 场比赛的光学跟踪数据上训练，轻量级 MLP 在所有评估指标上持续优于六个文献中的球员运动模型及学习可达凸包基线。研究还在篮球跟踪数据和单场 NFL 数据集上评估了跨运动迁移性。
正文 · 原文
Abstract:We study time-to-arrival (TTA) in sports: the shortest plausible time a player needs to reach an arbitrary location on the pitch given their current kinematic state, estimated as the lower envelope of observed locomotion times. Rather than modeling space ownership through position density estimation or closed-form physics-based models, we cast TTA as a conditional quantile regression problem, estimating the lower quantile of locomotion time so the model learns a player's lower-envelope arrival time. We train the model on optical tracking data from all 64 matches of the PFF FC FIFA World Cup 2022 dataset, and evaluate a lightweight multilayer perceptron against six player-motion models from the literature, re-expressed as TTA predictors, and a learned reachable- hull baseline. Across our evaluation metrics, the MLP consistently outperforms every physics-based baseline. To probe the generality of the learned locomotion model, we further evaluate it on basketball tracking data and a single-game NFL tracking dataset, analyzing the transferability of player motion dynamics across sports. Our project is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36183 [cs.AI] |
| (or arXiv:2610.36183v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36183 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Marc Gutiérrez-Pérez [view email]
[v1]
Mon, 26 Oct 2026 19:17:19 UTC (3,533 KB)
来源：arXiv cs.AI · arxiv.org