This project presents the design, implementation, and experimental evaluation of EYES ON — a low-cost, real-time computer vision system for automated tennis performance analysis. The system employs four synchronized commodity cameras combined with OpenCV-based image processing, background subtraction, physics-based 3D trajectory modelling, and a cloud-connected web dashboard to detect and classify tennis events including ball trajectory, bounce localization, stroke classification (forehand, backhand, serve), and line-call adjudication. The system achieves a shot detection rate of 99.7%, stroke classification accuracy of 97.1%, line-calling accuracy of 99.5%, and an average end-to-end latency of 152 ms — results that compare favourably with both state-of-the-art academic methods and leading commercial systems such as Hawk-Eye and PlaySight, at a fraction of the cost. This work addresses the affordability gap that prevents amateur and recreational tennis players from accessing the quantitative performance feedback available at the professional level, making professional-grade tennis analytics accessible to training centres and recreational facilities worldwide.This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy
yahya halawa (Mon,) studied this question.