Abstract
In the last decades, the automation process has improved humans' daily life. As technology continues to evolve, software and machines are increasingly being used to automate everyday tasks that were once performed by humans. For instance, sports have already seen some use of wearable devices in activities involving monitoring and measurement of position speed and body parameters, for example, heart rate and maximal oxygen consumption (VO2). Despite that, a personal trainer is still necessary to catch athlete's errors. Computer vision can be applied to this problem, helping athletes to improve their performances. Therefore, this work describes a low-cost computer vision algorithm to track a tennis ball's vertical motion. The algorithm aims to determine the touch position and to record a picture of the moment for analysis. The method works by applying color filters and transformations combined with a support vector machine to accurately detect the line and ball positions. The ball position is tracked along with different pictures, and a mathematical model is used to estimate the ball trajectory and helping predict the touching instant. An Extended Kalman Filter is applied in this work to reduce measurement error and improve detection performance by predicting future positions for the ball. The ground line positions are determined based on a Hough transform to detect the tennis court lines. The results have shown excellent performance and technical feasibility of the method for deployment in low cost embedded computers.
