# Locating Tennis Ball Impact on the Racket in Real Time Using an Event Camera

arXiv: https://arxiv.org/abs/2506.08327 · HTML: https://mlsanity.com/paper/2506.08327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (2019)
- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [Investigating Event-Based Cameras for Video Frame Interpolation in Sports](https://mlsanity.com/paper/2407.02370/) (2024)

## Built on by (most cited first, 1 of 1)

- [Event-based Batting Impact Estimation](https://mlsanity.com/paper/2605.25656/) (2026)

## Abstract (arXiv)

In racket sports, such as tennis, locating the ball's position at impact is important in clarifying player and equipment characteristics, thereby aiding in personalized equipment design. High-speed cameras are used to measure the impact location; however, their excessive memory consumption limits prolonged scene capture, and manual digitization for position detection is time-consuming and prone to human error. These limitations make it difficult to effectively capture the entire playing scene, hindering the ability to analyze the player's performance. We propose a method for locating the tennis ball impact on the racket in real time using an event camera. Event cameras efficiently measure brightness changes (called `events') with microsecond accuracy under high-speed motion while using lower memory consumption. These cameras enable users to continuously monitor their performance over extended periods. Our method consists of three identification steps: time range of swing, timing at impact, and contours of ball and racket. Conventional computer vision techniques are utilized along with an original event-based processing to detect the timing at impact (PATS: the amount of polarity asymmetry in time symmetry). The results of the experiments were within the permissible range for measuring tennis players' performance. Moreover, the computation time was sufficiently short for real-time applications.