## 1 Introduction  
Recently, studies related to broadcasting sports games, especially with Deep Learning methods to track players' movements in real time and combining TTS (Text-To-Speech) to make an AI-based broadcast, have been conducted a lot. Applications like Naver Sports supply users with text-form information about the events that have occurred during the game, and Fotmob allows users to know the score of the game in real time. NC soft suggested an application called PAIGE, which combines baseball games with TTS so that users can easily access realistic, emotional broadcasting of the game [1]. Moreover, pro sports clubs enhance their strategies and analyze opponents by using video analysis techniques, leading them to produce a better performance. Over the last few years, coaches and crowds have shown a vast amount of interest in game video analysis and it plays a huge part in analyzing and developing strategies. Famous clubs or leagues with a lot of capital support a vast number of cameras to gather the data during games. However, some leagues of clubs with a lower amount of capital have difficulty in gathering these data. Thus, it is necessary to develop a technique that allows them to recognize, track, and analyze strategy with videos without a high cost. To broadcast or analyze a sports game video with AI, recognizing the court, players, referee, balls (if included), actions of players, and referee and tracking them in real time are required. [PERSON] et al. suggested a method to detect highlights in an E-sports video by using LSTM multimodal training with highlight videos based on audio and chats [2]. To do this, the study used videos from the streaming site \"Twitch\", chat logs and audio data, and other highlight video data from YouTube and gaming companies. With this, they trained the model to detect the highlights of the video. As a result, the model showed a 90.68% accuracy of detecting the highlight scenes with a 0.722 F1 score. [PERSON] and [PERSON] suggested a model to detect highlight scenes in E-Sports videos [3]. They used total score and object score information on screen. With the data provided by the gaming companies, they analyzed them with OpenCV and CNN and obtained an overall 89.9% accuracy. [PERSON] et al. suggested a model to detect highlight scenes using a CNN model and chat logs and video screens [4]. This model showed an accuracy of 74.7%. Furthermore, integrating these single techniques into one and building it into a system is required. This study allows us to recognize the court, player actions and tracking, and game events and generate according sentences based on generative AI to make it possible for an AI to broadcast a sports game. To extract the features from each frame in a video and match them for panorama image shift, the SIFT (Scale-Invariant Feature Transform) algorithm is applied, and the homography is generated with panoramic images and is combined with the videos from multi-view cameras in a local view [5]. Furthermore, for court recognition, segmentation based on the YOLO model is applied. Player action and ball detection is performed through YOLO algorithms. YOLO integrates techniques such as dynamic anchor assignment and adaptive task prioritization to improve its architecture, enhance accuracy, and achieve faster inference speeds [6; 7]. For object tracking, we propose the following algorithm: In each frame, the YOLO detection model is used to identify the bounding boxes of players. Subsequently, the Intersection over Union (IoU) is calculated between the bounding boxes from the current frame and those from the previous five frames. The bounding boxes with the highest IoU are then linked together to track the movement paths of the players. In recognizing players in a game, since there are multiple teams in a game, team colors, the number of players and referees, and the layout of the court are saved and used in domain knowledge. Finally, based on the information extracted from the video, the ChatGPT-4.0 Turbo API was used to generate a sports broadcast.