Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction

TL;DR

This paper introduces a real-time egocentric system using event cameras and eye-gaze data to predict ping pong ball trajectories with high accuracy and low latency, outperforming traditional frame-based methods.

Contribution

The paper presents the first egocentric, event-camera-based approach for real-time ping pong trajectory prediction leveraging foveated vision and ground-truth dataset collection.

Findings

Achieves 4.5 ms latency in detection pipeline.

Reduces computational load by a factor of 10.81.

Successfully predicts 3D ball trajectories in real-time.

Abstract

In this paper, we present a real-time egocentric trajectory prediction system for table tennis using event cameras. Unlike standard cameras, which suffer from high latency and motion blur at fast ball speeds, event cameras provide higher temporal resolution, allowing more frequent state updates, greater robustness to outliers, and accurate trajectory predictions using just a short time window after the opponent's impact. We collect a dataset of ping-pong game sequences, including 3D ground-truth trajectories of the ball, synchronized with sensor data from the Meta Project Aria glasses and event streams. Our system leverages foveated vision, using eye-gaze data from the glasses to process only events in the viewer's fovea. This biologically inspired approach improves ball detection performance and significantly reduces computational latency, as it efficiently allocates resources to the most perceptually relevant regions, achieving a reduction factor of 10.81 on the collected trajectories. Our detection pipeline has a worst-case total latency of 4.5 ms, including computation and perception - significantly lower than a frame-based 30 FPS system, which, in the worst case, takes 66 ms solely for perception. Finally, we fit a trajectory prediction model to the estimated states of the ball, enabling 3D trajectory forecasting in the future. To the best of our knowledge, this is the first approach to predict table tennis trajectories from an egocentric perspective using event cameras.