Accurate Spin Estimation of Table Tennis Balls Using an Event Camera

Core Concepts

This work presents a real-time and accurate method for estimating the spin of table tennis balls using an event camera.

Abstract

The authors propose a method for estimating the spin of table tennis balls using an event camera. The key contributions are: Tracking the ball in real-time and extracting events generated by the ball's logo. A real-time and accurate method for estimating the table tennis ball's spin magnitude and axis. Evaluation using a ball spinner to show the potential of the approach and a deployment with a ball thrower to demonstrate the method in a real setup. The method works by first tracking the ball using the Exponential Reduced Ordinal Surface (EROS) event representation. This allows for continuous and asynchronous updates from the event stream. The ball's position, velocity, and radius are estimated using a Kalman filter. Next, the events generated by the ball's logo are extracted based on the estimated ball properties. Optical flow is then computed on these extracted logo events to infer the ball's spin magnitude and axis. The authors evaluate their method in two settings: a ball spinner and a ball thrower. With the ball spinner, they achieve a spin magnitude mean error of 10.7 ± 17.3 rps and a spin axis mean error of 32.9 ± 38.2°. When deployed with the ball thrower, their method achieves a success rate slightly above a state-of-the-art frame-based approach, with comparable spin magnitude and axis estimation errors. The authors also discuss the limitations of their approach, noting that the method struggles with certain logo designs and orientations. They suggest potential solutions, such as using a higher resolution event camera or a longer focal length lens.

Stats

The ball spinner experiments reported a spin magnitude mean error of 10.7 ± 17.3 rps and a spin axis mean error of 32.9 ± 38.2°. The ball thrower experiments reported a success rate slightly above a state-of-the-art frame-based approach, with a spin magnitude mean error of 10.7 ± 17.3 rps and a spin axis mean error of 32.9 ± 38.2°.

Quotes

"Spin plays a pivotal role in ball-based sports. Estimating spin becomes a key skill due to its impact on the ball's trajectory and bouncing behavior." "Event cameras do not suffer as much from motion blur, thanks to their high temporal resolution. Moreover, the sparse nature of the event stream solves communication bandwidth limitations many frame cameras face."

Key Insights Distilled From

Table tennis ball spin estimation with an event camera

by Thomas Gossa... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09870.pdf

Deeper Inquiries

How could the proposed method be extended to other ball sports beyond table tennis?

The proposed method for table tennis ball spin estimation using an event camera can be extended to other ball sports by adapting the algorithm to the specific characteristics of each sport. For example, in sports like tennis or baseball, where the ball is larger and the spin dynamics may differ, the event-based spin estimation approach would need to be adjusted to account for these differences. This could involve modifying the optical flow algorithms to accommodate the size and speed of the ball, as well as the specific spin patterns typical in each sport. Additionally, the event camera settings, such as bias values, would need to be optimized for the new sport to ensure accurate event detection and spin estimation.

What are the potential challenges in adapting the event-based spin estimation approach to different ball sizes and materials?

Adapting the event-based spin estimation approach to different ball sizes and materials presents several challenges. One major challenge is the variation in the appearance and behavior of the ball based on its size and material composition. Different ball sizes may result in varying logo visibility and event generation patterns, requiring adjustments in the event extraction and optical flow algorithms. Additionally, the material of the ball can affect how events are generated and detected by the camera, potentially impacting the accuracy of spin estimation. Furthermore, different ball materials may interact with the camera sensor differently, leading to variations in event detection and optical flow estimation. Calibration and fine-tuning of the event camera settings, such as bias values and event detection thresholds, would be crucial to account for these differences and ensure reliable spin estimation across various ball sizes and materials.

How could the event-based spin estimation be combined with other sensing modalities, such as inertial measurement units, to further improve the accuracy and robustness of the system?

Integrating event-based spin estimation with other sensing modalities, such as inertial measurement units (IMUs), can enhance the accuracy and robustness of the system by providing complementary information about the ball's motion and spin. IMUs can capture data on the ball's acceleration, orientation, and angular velocity, which can be used in conjunction with event-based optical flow to improve spin estimation. By fusing data from event cameras and IMUs, the system can leverage the strengths of each sensor modality. IMU data can provide additional insights into the ball's rotational dynamics, especially in cases where the event camera may have limitations in capturing certain spin patterns or speeds. The combined sensor data can be fused using sensor fusion algorithms to generate more accurate and reliable estimates of the ball's spin magnitude and axis. Overall, integrating event-based spin estimation with IMUs can offer a comprehensive and robust solution for analyzing ball spin in sports, enhancing performance analysis, player training, and sports broadcasting applications.