Abstract
Spin plays a pivotal role in ball-based sports. Estimating spin becomes a key skill due to its impact on the ball's trajectory and bouncing behavior. Spin cannot be observed directly, making it inherently challenging to estimate. In table tennis, the combination of high velocity and spin renders traditional low frame rate cameras inadequate for quickly and accurately observing the ball's logo to estimate the spin due to the motion blur. Event cameras do not suffer as much from motion blur, thanks to their high temporal resolution. Moreover, the sparse nature of the event stream solves communication bandwidth limitations many frame cameras face. To the best of our knowledge, we present the first method for table tennis spin estimation using an event camera. We use ordinal time surfaces to track the ball and then isolate the events generated by the logo on the ball. Optical flow is then estimated from the extracted events to infer the ball's spin. We achieved a spin magnitude mean error of $10.7 \pm 17.3$ rps and a spin axis mean error of $32.9 \pm 38.2°$ in real time for a flying ball.
Abstract (translated)
旋转在球类运动中扮演着关键角色。由于其对球轨迹和弹起行为的影响，估计旋转成为了一个关键技能。由于无法直接观察到旋转，因此估计旋转本质上具有挑战性。在乒乓球中，高速度和高旋转使得传统低帧率相机无法快速且准确地观察到球的标志，从而导致运动模糊。事件相机由于其高时间分辨率，没有像事件相机那样受到运动模糊的影响。此外，事件流稀疏的特性解决了许多帧相机面临的通信带宽限制。据我们所知，我们首先提出了一种使用事件相机进行乒乓球旋转估计的方法。我们使用序时表面跟踪球，然后从球上估计标志的事件。然后通过提取这些事件估计球的旋转。我们可以在实时飞行球中实现球旋转 magnitude 平均误差为 $10.7 \pm 17.3$ rps 和轴旋转平均误差为 $32.9 \pm 38.2^\circ$。
URL
https://arxiv.org/abs/2404.09870
