AI Model: Deep Learning Model to Classify the Tennis Ball Position and Create the Sports Commentary

The incorporation of Artificial Intelligence (AI) into sport has hugely increased the accuracy and efficiency of decision-making systems. AI-based Sports Commentary System," intends to implement a ball position classification in automatic form utilizing top-tier computer vision and deep learning approaches, to determine if the ball is in or out of court while a game is on, especially in the fast game like tennis. The proposed system uses YOLOv5, a real-time object detection algorithm, to precisely detect and follow the ball in video frames. A Convolution Neural Network and ResNet50 models are experimented to classify the ball's position as In or Out. The deep learning models are trained on the dataset of tennis court images as in/out. The experimental outcome shows indicate that accuracy of ResNet50 is greater when compared to standard CNN architectures. This solution gives an economical, scalable, and reliable replacement for traditional line judging, with possible use in tennis. The models are trained with the video frames, and the proposed User Interface project can handle both live video streams and uploaded videos which includes the frame extraction module. The Text To Speech (TTS) system generates synchronized live audio commentary using the pyttsx3 Text-to-Speech engine, producing a fully processed output video with real-time insights limited to the ball position In and Out.