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

arXiv 2506.08327 ↗First posted 10 Jun 2025cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

7 of 25 references.

References · 7

  1. 2019Event-based Vision: A Surveycited by 1,016
  2. 2018HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classificationcited by 218
  3. 2018A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimationcited by 178
  4. 2022Secrets of Event-Based Optical Flowcited by 92
  5. 2019Event-Based Motion Segmentation by Motion Compensationcited by 78
  6. 2024Table tennis ball spin estimation with an event cameracited by 11
  7. 2024Investigating Event-Based Cameras for Video Frame Interpolation in Sportscited by 1

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Citing papers per year: 2026: 1.

1 paper.