Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games

2026-06-25 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

An event-based active vision system has been developed for accurate real-time spin estimation of unmodified balls in professional ball games. This system integrates an event camera for high temporal resolution, high-speed pan/tilt galvanometer mirrors for continuous tracking, and a low-latency focus-tunable telephoto lens to maintain spatial resolution and focus. Ball tracking employs a hybrid approach combining 2D event-based detection with 3D localization. For high-accuracy offline spin estimation, the s-CMax method performs contrast maximization on the sphere, achieving mean magnitude and axis errors of 2.1% and 4.0 degrees, respectively, on static balls across table tennis, baseball, tennis, and golf. A low-latency online method, demonstrated in professional table tennis, utilizes an uncertainty-aware convolutional neural network trained on pseudo-ground-truth from the offline approach, refined by GPU-accelerated contrast maximization. This real-time setup achieves 8.8% magnitude and 6.4 degrees axis mismatch, 3 ms latency, and 750 Hz throughput with a three-view configuration.

Key takeaway

For sports analytics engineers developing real-time performance tracking systems, this event-based gaze control system offers a robust solution for accurate ball spin estimation. You can achieve high precision (8.8% magnitude, 6.4 degrees axis mismatch) with extremely low latency (3 ms) and high throughput (750 Hz), even with unmodified balls. Consider integrating event cameras and hybrid tracking for your next-generation sports vision applications to capture critical spin data.

Key insights

Combining event cameras with active vision and hybrid spin estimation enables accurate real-time ball spin measurement.

Principles

• Event cameras excel in high-speed, low-blur scenarios.
• Hybrid 2D/3D tracking improves ball localization.
• Offline methods can generate pseudo-ground-truth for online training.

Method

The system uses an event camera, pan/tilt mirrors, and a focus-tunable lens for tracking, then applies s-CMax for offline spin or a CNN with GPU-accelerated contrast maximization for online.

In practice

• Track unmodified balls in professional sports.
• Measure spin in table tennis, baseball, tennis, golf.
• Achieve 3 ms latency for real-time applications.

Topics

• Event Cameras
• Spin Estimation
• Active Vision Systems
• Real-time Tracking
• Table Tennis Analytics
• Sports Vision

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.