Abstract
Pacing strategies, defined by velocity and stroke rate profiles, are essential for peak performance in canoe sprint. While GPS is the gold standard for analysis, its limited availability necessitates automated video-based solutions. This paper presents an extended framework for reconstructing performance metrics from panned and zoomed video recordings across all sprint disciplines (K1-K4, C1-C2) and distances (200m-500m). Our method utilizes YOLOv8 for buoy and athlete detection, leveraging the known buoy grid to estimate homographies. We generalized the estimation of the boat position by means of learning a boat-specific athlete offset using a U-net based boat tip calibration. Further, we implement a robust tracking scheme using optical flow to adapt to multi-athlete boat types. Finally, we introduce methods to extract stroke rate information from either pose estimations or the athlete bounding boxes themselves. Evaluation against GPS data from elite competitions yields a velocity RRMSE of 0.020 +- 0.011 (rho = 0.956) and a stroke rate RRMSE of 0.022 +- 0.024 (rho = 0.932). The methods provide coaches with highly accurate, automated feedback without requiring on-boat sensors or manual annotation.
Abstract (translated)
划船竞速中的速度和划桨频率策略对于达到巅峰表现至关重要。虽然全球定位系统（GPS）是分析的最佳标准，但由于其有限的可用性，需要自动化的视频解决方案。本文提出了一种扩展框架，用于从平移和缩放视频记录中重建所有冲刺项目（K1-K4, C1-C2）及距离（200米至500米）的性能指标。 我们的方法利用YOLOv8检测浮标和运动员，并通过已知的浮标网格来估计同构映射。我们通过学习特定于船的运动员偏移量，采用基于U-net的船尖校准方法，推广了对船位置的估算。此外，我们实施了一种使用光流实现的鲁棒跟踪方案，以适应多运动员类型的船只。 最后，我们引入了几种从姿势估计或运动员边界框自身提取划桨频率信息的方法。与来自精英比赛的GPS数据对比评估显示，速度的相对均方根误差（RRMSE）为0.020 ± 0.011 (ρ = 0.956)，划桨频率的RRMSE为0.022 ± 0.024 (ρ = 0.932)。 这些方法为教练提供了高精度且自动化的反馈，无需船上传感器或手动标注。
URL
https://arxiv.org/abs/2602.22941
