用视频自动估算皮划艇速度和划桨频率，精度媲美GPS
Velocity and stroke rate reconstruction of canoe sprint team boats based on panned and zoomed video recordings
- 基于YOLOv8和U-net，从变焦镜头中精准定位艇身位置
- 速度与划桨率误差均低于1.4%，相关性超0.97
- 无需传感器，适合教练日常训练分析
皮划艇竞速中的配速策略（由速度和划桨频率决定）对成绩至关重要。尽管GPS是分析金标准，但其可用性受限，亟需自动化视频分析方案。本文提出一个扩展框架，可处理所有竞速项目（K1-K4、C1-C2）和距离（200米-500米）的平移与变焦视频。方法采用YOLOv8检测浮标与运动员，利用已知浮标网格估计单应性；通过基于U-net的艇头校准学习艇身特定偏移量，实现船位泛化估计；结合光流实现多运动员艇型的鲁棒跟踪；并从姿态估计或运动员边界框中提取划桨频率。在精英赛事的GPS数据上评估，速度平均绝对百分比误差（MAPE）为0.011 [0.008, 0.014]（斯皮尔曼相关系数=0.974），划桨率MAPE为0.009 [0.006, 0.013]（相关系数=0.975）。该方法为教练提供高精度、全自动的反馈，仅需极少人工初始化，且无需传感器。
原文摘要 · Abstract (English)
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 MAPE of 0.011 [0.008 0.014] (Spearman rho=0.974) and a stroke rate MAPE of 0.009 [0.006 0.013] (Spearman rho = 0.975). The methods provide coaches with highly accurate, automated feedback with minimal manual initialization work required, and without requiring sensors.
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