Improving the Accuracy of the Annotation Algorithm in Pattern-Based Tennis Game Video

Automatically annotating the game of tennis using video playback is a high potential but has many challenges. In this research, deep learning in annotating tennis games with the integration of computer vision and machine learning is discussed. The experiments of this research are performed using a set of video images and the implementation of the CNN algorithm. The proposed method was compared with NAIVE BAYES, SVM, HMM, and S-SVM methods. The results show that well-tuned channel neural networks have the best performance among the strategies. U sing deep neural network convolution in Comparisons and evaluations showed that annotation is performed with great accuracy. The accuracy obtained in this study is 0.92. CNN's proposed algorithm showed that with the necessary changes in network parameters, and this algorithm's techniques, the desired result achieved, and accuracy greatly increased.