用事件相机精准捕捉击球瞬间，比传统方法误差降低63%。
Event-based Batting Impact Estimation
- 基于事件相机的加权质心距离计算击球时间
- 在弱光和遮挡下仍保持高精度，误差降低63%
- 适合需要微秒级时间捕捉的运动分析场景
精确估计击球时机对理解快速传感运动控制至关重要。然而，由于时间分辨率不足和运动模糊，RGB相机难以胜任此任务；而惯性测量单元（IMUs）因传感器侵入性和有限的时间精度，在实际比赛中不实用。为克服这些局限，本文提出一种新型框架，利用具备微秒级分辨率和高动态范围的事件相机，基于检测到的球与球拍之间的加权质心距离估计击球时间。为缓解事件帧与RGB图像间的领域差异导致的分割精度下降问题，我们生成高密度事件帧，并引入一种利用这些帧及双向掩码信息的掩码优化网络，采用新型损失函数进行训练。在真实数据集上的实验表明，该方法在弱光环境和严重遮挡条件下均表现优异，相比基线方法平均绝对误差降低约63%。
原文摘要 · Abstract (English)
Estimating the precise timing of batting impact is crucial for understanding the rapid sensorimotor control. However, this task is challenging for RGB cameras due to insufficient temporal resolution and motion blur. Similarly, Inertial Measurement Units (IMUs) are impractical for actual matches due to sensor intrusiveness and their limited temporal precision. To overcome these limitations, we propose a novel framework leveraging event-based cameras, which offer microsecond resolution and high dynamic range, to estimate impact timing based on the weighted centroid distance between the detected ball and bat. To address the domain gap between event frames and RGB images that degrades segmentation accuracy, we generate high-density event frames. We then introduce a mask refinement network that leverages these frames and bidirectional mask information, optimized using a novel loss function. Experiments on real-world datasets demonstrate that our method achieves superior accuracy under challenging conditions, including low-light environments and severe occlusions, outperforming baselines by reducing the Mean Absolute Error by approximately 63%.
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