Event-based Batting Impact Estimation
2026-05-25 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new way to precisely measure when a bat hits a ball using event-based cameras, which capture very fast motion better than regular cameras. They made a system that can detect the bat and ball even when it's dark or the view is blocked. To improve accuracy, they created a special method to better identify objects in the event camera data. Their approach was much more accurate than older methods at figuring out the exact impact moment in sports.
Event-based camerasBatting impact timingSensorimotor controlMotion blurRGB camerasInertial Measurement Units (IMUs)SegmentationMask refinement networkMean Absolute ErrorDomain gap
Authors
Ryotaro Ishida, Wataru Ikeda, Ryosei Hara, Akemi Kobayashi, Toshitaka Kimura, Mariko Isogawa
Abstract
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%.