论文标题
一种计算机视觉方法，用于估计跳跃速度
A Computer Vision Method for Estimating Velocity from Jumps
论文作者
论文摘要
运动员通常会进行健身评估，以评估他们的训练进度。通常，这些评估需要训练有素的专业人员，该专业人员使用专业设备（例如力板）。为了进行评估，运动员进行下降和蹲下的跳跃，并测量关键变量，例如速度，飞行时间和稳定时间，仅举几例。但是，业余运动员可能无法获得可以提供这些评估的专业人员或设备。在这里，我们研究了使用视频记录估算关键变量的可行性。我们将重点放在跳速作为起点，因为它与其他关键变量高度相关，对于确定姿势和较低的LIMB容量很重要。我们发现，可以在一系列运动员中以高度的精度估算速度，平均R值为0.71（SD = 0.06）。
Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment like force plates. For the assessment, athletes perform drop and squat jumps, and key variables are measured, e.g. velocity, flight time, and time to stabilization, to name a few. However, amateur athletes may not have access to professionals or equipment that can provide these assessments. Here, we investigate the feasibility of estimating key variables using video recordings. We focus on jump velocity as a starting point because it is highly correlated with other key variables and is important for determining posture and lower-limb capacity. We find that velocity can be estimated with a high degree of precision across a range of athletes, with an average R-value of 0.71 (SD = 0.06).
