Event-based Batting Impact Estimation
This work provides a practical solution for precise impact timing estimation in sports, which is important for understanding sensorimotor control, though it is domain-specific.
The paper tackles the problem of estimating batting impact timing from video, which is difficult due to motion blur and low temporal resolution of RGB cameras. Using event-based cameras and a mask refinement network, they reduce Mean Absolute Error by approximately 63% compared to baselines.
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%.