Efficient Ball Position Estimation for Tennis Court Robot Assistants using DualCamera System

During tennis training, professional players use many balls that will be distributed randomly around the tennis court.Collecting the ball can be done manually, However, the effort and the time make it inefficient.The mobile robot for ball collection has been introduced as a solution to save power and training session time.The robot tasks involve several steps: ball detection, estimating their positions, and finding the best path for efficient collection.In our previous work, we addressed the ball detection issue utilizing a neural network algorithm using YOLOv8.This study focuses on the next step, ball position estimation, using two cameras to cover the entire court.The results demonstrate successful ball position estimation along the x-axis and y-axis, achieving an accuracy of 97.76 %.

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Publication Details

Journal
Proceedings of International Conference on Artificial Life and Robotics
Published
2025-02-13
DOI
https://doi.org/10.5954/icarob.2025.os12-9
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Efficient Ball Position Estimation for Tennis Court Robot Assistants using DualCamera System

Kazuo Ishii, Raji Alahmad, Hussam Alraie, Mohammad Albaroudi et al.
Proceedings of International Conference on Artificial Life and Robotics
Vehicle License Plate Recognition
article

Efficient Ball Position Estimation for Tennis Court Robot Assistants using DualCamera System

Kazuo Ishii, Raji Alahmad, Hussam Alraie, Mohammad Albaroudi, Abdullah Alraee
article en

Abstract

During tennis training, professional players use many balls that will be distributed randomly around the tennis court.Collecting the ball can be done manually, However, the effort and the time make it inefficient.The mobile robot for ball collection has been introduced as a solution to save power and training session time.The robot tasks involve several steps: ball detection, estimating their positions, and finding the best path for efficient collection.In our previous work, we addressed the ball detection issue utilizing a neural network algorithm using YOLOv8.This study focuses on the next step, ball position estimation, using two cameras to cover the entire court.The results demonstrate successful ball position estimation along the x-axis and y-axis, achieving an accuracy of 97.76 %.

Proceedings of International Conference on Artificial Life and RoboticsVol. 30
Kyushu Institute of Technology (JP)
Affordable and clean energy
Openalex Percentile: Top 70%
Vehicle License Plate Recognition
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